trial environment is operated in Ylivieska [34, 35], but no commercial deployments of LSA in 2.3 GHz band are available yet. A service pilot with LSA radio licenses to commercial end users operating with incumbent wireless cameras in the 2.3 GHz band was announced in the Netherlands in May 2016 [36], and more pilots are expected in the near future. The LTE MNOs are expected to make fairly static multiyear spectrum sharing contracts with the incumbents to justify investments in building mobile network infrastructure for LSA operation [37]. LSA

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A concept called SAS is in development in the USA. It is very similar to LSA, as both of them include incumbent users and licensed shared users who have exclusive shared access to the spectrum. The Licensed Shared Access in SAS is known as Priority Access License (PAL). LSA excludes opportunistic access where no protection from incumbents is provided, but SAS adds an additional third tier for unlicensed opportunistic spectrum access with General Authorized Access (GAA), as shown in **Figure 2**. PAL users are protected from interference

The SAS design ensures protection also for the incumbents who cannot provide a priori information to a central database. This is a major difference to LSA, where this information has to be communicated to a central database (LSA Repository) in order to protect the incumbents. The incumbents operating in the Citizens Broadband Radio Service (CBRS) band include military services whose information is too sensitive to be stored in a database. Instead, SAS includes Environmental Sensing Capability (ESC) component which uses spectrum sensing to provide the needed data for spectrum access decisions. As [38] states, spectrum sensing is not a trivial matter, especially with the strict requirements in SAS. ESC will not be used in the first phase of SAS deployment, which restricts the SAS operation in the zones with military incumbents near coastal areas until a suitable ESC technology is available. ESC technologies have

could also provide mechanisms to mitigate intra-MNO-system interference [37].

**Figure 2.** Overview of the level of access rights in different tiers of SAS and LSA sharing models.

from GAA tier, but not from the incumbents.

As defined in [23], LSRAI has the following characteristics:

	- Space parameters describing the geographical area to which the restriction applies.
	- Frequency parameters describing the frequency range to which the restriction applies.
	- Time parameters describing when the restriction applies.
	- Radio parameters describing the RF restrictions to be applied within the space/frequency/time combination defined by the above parameters

## **3. The status of LSA and SAS**

This section considers the feasibility, current status, and evolution of LSA and briefly compares it to the US concept for licensed shared spectrum access; Spectrum Access System (SAS). The work on LSA has been very active in regulation and standardization: CEPT Reports [24– 26], ECC harmonized conditions for the use of the 2.3 GHz band in [15, 27–29], and European Telecommunications Standards Institute (ETSI) standardization in [22, 23, 30, 31] provide all the measures needed for a National Regulatory Authorities in a CEPT country to create an implementation of LSA. A regulatory evaluation in [32] concluded that LSA implementations are feasible as they provide a simple spectrum sharing approach providing a high degree of certainty for both the incumbent and the LSA licensee with low impact to the systems and the concept has already been tested and approved. The use of LSA is a national matter, which does not require modifications to the ITU-R Radio Regulations (RR) but needs to comply with the current regulations.

A study on the feasibility of LSA from business perspective [33] concluded that LSA implementations could be profitable for MNOs in Finland if they have a reasonably good customer base and well-defined network launch and management. Most importantly, the MNO has to carefully investigate the techno-economics to see if there is a customer base large enough to justify the investments in new spectrum and network resources. A Finnish LSA trial environment is operated in Ylivieska [34, 35], but no commercial deployments of LSA in 2.3 GHz band are available yet. A service pilot with LSA radio licenses to commercial end users operating with incumbent wireless cameras in the 2.3 GHz band was announced in the Netherlands in May 2016 [36], and more pilots are expected in the near future. The LTE MNOs are expected to make fairly static multiyear spectrum sharing contracts with the incumbents to justify investments in building mobile network infrastructure for LSA operation [37]. LSA could also provide mechanisms to mitigate intra-MNO-system interference [37].

LSA Spectrum Resource Availability Information (LSRAI) contains the information on the LSA spectrum resource that may be used by the LSA licensee. LSRAI is generated in

protocol messages as defined in the ETSI technical specification [23]. The LSA Information Exchange Function to maintain the LSRAI synchronization between LSA Controllers and

• It contains one or more zones. A zone is an information object which describes a set of

○ Space parameters describing the geographical area to which the restriction applies.

○ Frequency parameters describing the frequency range to which the restriction applies.

○ Radio parameters describing the RF restrictions to be applied within the space/fre-

This section considers the feasibility, current status, and evolution of LSA and briefly compares it to the US concept for licensed shared spectrum access; Spectrum Access System (SAS). The work on LSA has been very active in regulation and standardization: CEPT Reports [24– 26], ECC harmonized conditions for the use of the 2.3 GHz band in [15, 27–29], and European Telecommunications Standards Institute (ETSI) standardization in [22, 23, 30, 31] provide all the measures needed for a National Regulatory Authorities in a CEPT country to create an implementation of LSA. A regulatory evaluation in [32] concluded that LSA implementations are feasible as they provide a simple spectrum sharing approach providing a high degree of certainty for both the incumbent and the LSA licensee with low impact to the systems and the concept has already been tested and approved. The use of LSA is a national matter, which does not require modifications to the ITU-R Radio Regulations (RR) but needs to comply with

A study on the feasibility of LSA from business perspective [33] concluded that LSA implementations could be profitable for MNOs in Finland if they have a reasonably good customer base and well-defined network launch and management. Most importantly, the MNO has to carefully investigate the techno-economics to see if there is a customer base large enough to justify the investments in new spectrum and network resources. A Finnish LSA

interface using LSA1

the LSA Repository and sent to the LSA Controller over the LSA1

LSA Repositories and the LSA1 protocol are described in detail in [23].

operational conditions or restrictions to be applied by the LSA licensee.

• A zone has a zone type associated to it (e.g., restriction, protection, exclusion).

As defined in [23], LSRAI has the following characteristics:

60 Cognitive Radio in 4G/5G Wireless Communication Systems

• A zone contains space, frequency, radio, and time parameters:

○ Time parameters describing when the restriction applies.

**3. The status of LSA and SAS**

the current regulations.

quency/time combination defined by the above parameters • A zone has a zone ID and a zone configuration index associated to it. A concept called SAS is in development in the USA. It is very similar to LSA, as both of them include incumbent users and licensed shared users who have exclusive shared access to the spectrum. The Licensed Shared Access in SAS is known as Priority Access License (PAL). LSA excludes opportunistic access where no protection from incumbents is provided, but SAS adds an additional third tier for unlicensed opportunistic spectrum access with General Authorized Access (GAA), as shown in **Figure 2**. PAL users are protected from interference from GAA tier, but not from the incumbents.

The SAS design ensures protection also for the incumbents who cannot provide a priori information to a central database. This is a major difference to LSA, where this information has to be communicated to a central database (LSA Repository) in order to protect the incumbents. The incumbents operating in the Citizens Broadband Radio Service (CBRS) band include military services whose information is too sensitive to be stored in a database. Instead, SAS includes Environmental Sensing Capability (ESC) component which uses spectrum sensing to provide the needed data for spectrum access decisions. As [38] states, spectrum sensing is not a trivial matter, especially with the strict requirements in SAS. ESC will not be used in the first phase of SAS deployment, which restricts the SAS operation in the zones with military incumbents near coastal areas until a suitable ESC technology is available. ESC technologies have

**Figure 2.** Overview of the level of access rights in different tiers of SAS and LSA sharing models.

already been developed and demonstrated in SAS trials [39]. Unlike LSA, SAS standardization is still in progress, but the industrial interest in CBRS Alliance [40] is strong and advances are expected in the near future. The first commercial SAS deployments are expected during 2018 [41] in 3.55–3.7 GHz CBRS [42] band in the USA. **Table 1** gives a brief comparison of the key features of the LSA and SAS concepts. More detailed considerations and comparisons from both technical and business perspectives are given in [43, 44].

A concept called LSA evolution is currently under development. The use of spectrum sensing is considered to provide more dynamic version of LSA, which is needed for the novel 5G use cases [49, 50]. The original LSA specification assumes that the spectrum is available for the operator always when the incumbent does not use the spectrum. The spectrum is available for the operator within the regulative area, like country borders, excluding the areas where the incumbent uses the spectrum. From spectrum perspective, high QoS is achieved when the

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The interest in private LTE and 5G networks has grown due to the increased number of IMT frequency bands, higher frequency ranges, variety in spectrum assignments for 3GPP technologies, and revolution of wireless industrial communication [51]. The feasibility study [52] addresses these issues and applies learning from the later developed SAS/CBRS system at the same time. The study considers, for example, how to provide temporary spectrum access for

The new concepts for LSA evolution include short-term license periods, possibility to allocate spectrum locally, and supporting co-primary sharing, which can guarantee the quality of service from spectrum perspective [49, 53]. Most LTE use is static, when the spectrum assignments are considered. Even if the user equipments (UEs) are mobile, the spectrum use is more determined by the eNodeBs. They traditionally require masts, electricity, backhaul connectiv-

The temporary LTE or 5G spectrum access is most likely to be related to PMSE, Public Protection and Disaster Relief (PPDR), or Test and Development (T&D) licenses. The current mobile networks are wide area networks even if they are built for capacity. Most private LTE and 5G networks are local. PMSE, PPDR, and T&D networks are both temporary and local

This section presents a trial of LSA evolution system developed for 5G PMSE use case. The trial focuses on utilizing LSA for sharing spectrum in 2.3–2.4 GHz band between wireless cameras (PMSE) and mobile network operator (MNO) serving users. When the spectrum is required by the incumbents, such as wireless video cameras during a sports event, the transmissions of the mobile network in this area need to be controlled to allow the operation of the wireless cameras in the band. The mobile network base stations on this band can be shut

The developed LSA evolution system allows to set priorities for different users of the spectrum, and thus it is possible, for example, to give the highest priority to the old/proprietary PMSE systems which cannot communicate bidirectionally with the spectrum manager, which includes the functionalities of LSA Controller and LSA Repository. The LTE/5G-based equipment can be controlled (their transmission frequencies and power levels adjusted) so that no interference is caused toward the old/proprietary PMSE equipment or other LTE/5G-based

down or their transmission powers and potentially operating frequencies controlled.

incumbent does not use the spectrum.

local high-quality wireless networks.

ity, and professional installers.

equipment.

and thus can benefit most from LSA evolution.

**4. LSA evolution systvem for 5G PMSE use case trial**

LSA and SAS are currently defined for use only in the mentioned frequency bands with their specific incumbents, but the basic operational principles are straightforward to adopt to other sub-6 GHz frequency bands, where spectrum sharing is more relevant. Spectrum sharing is less relevant in higher frequency ranges, such as mmWaves, where wireless communication is not so much limited by interference, but the higher path losses. The ETSI LSA standardization was done partly in liaison with the 3rd Generation Partnership Project (3GPP) [45], which has studied how LSA could provide a global solution for a 3GPP MNO in [46]. LSA has also been recognized as one of the future technology trends for IMT in the ITU-R Working Party 5D on IMT systems [47].

5G and its new use cases require a more dynamic approach to access shared spectrum resources than ETSI LSA for 2.3–2.4 GHz band can provide. Spectrum sensing techniques are needed as the more dynamic access to spectrum cannot be achieved by using static a priori information. The dominant problems in spectrum sensing are the removal of shadowing and multipath fading. Methods to overcome these problems through cooperative mobile measurements to create interference maps are discussed in [48], but the current technologies related to spectrum sensing are still not able to guarantee protection from harmful interference [43].

**Table 1.** Brief comparison of LSA and SAS concepts.

A concept called LSA evolution is currently under development. The use of spectrum sensing is considered to provide more dynamic version of LSA, which is needed for the novel 5G use cases [49, 50]. The original LSA specification assumes that the spectrum is available for the operator always when the incumbent does not use the spectrum. The spectrum is available for the operator within the regulative area, like country borders, excluding the areas where the incumbent uses the spectrum. From spectrum perspective, high QoS is achieved when the incumbent does not use the spectrum.

already been developed and demonstrated in SAS trials [39]. Unlike LSA, SAS standardization is still in progress, but the industrial interest in CBRS Alliance [40] is strong and advances are expected in the near future. The first commercial SAS deployments are expected during 2018 [41] in 3.55–3.7 GHz CBRS [42] band in the USA. **Table 1** gives a brief comparison of the key features of the LSA and SAS concepts. More detailed considerations and comparisons

LSA and SAS are currently defined for use only in the mentioned frequency bands with their specific incumbents, but the basic operational principles are straightforward to adopt to other sub-6 GHz frequency bands, where spectrum sharing is more relevant. Spectrum sharing is less relevant in higher frequency ranges, such as mmWaves, where wireless communication is not so much limited by interference, but the higher path losses. The ETSI LSA standardization was done partly in liaison with the 3rd Generation Partnership Project (3GPP) [45], which has studied how LSA could provide a global solution for a 3GPP MNO in [46]. LSA has also been recognized as one of the future technology trends for IMT in the ITU-R Working Party 5D on

5G and its new use cases require a more dynamic approach to access shared spectrum resources than ETSI LSA for 2.3–2.4 GHz band can provide. Spectrum sensing techniques are needed as the more dynamic access to spectrum cannot be achieved by using static a priori information. The dominant problems in spectrum sensing are the removal of shadowing and multipath fading. Methods to overcome these problems through cooperative mobile measurements to create interference maps are discussed in [48], but the current technologies related to spectrum sensing are still not able to guarantee protection from harmful interference [43].

Tiers Two tiers with individual access 3-tier system; two tiers with individual access and

a third license-exempt tier

technologies

licenses

Centralized geolocation database with information based on spectrum sensing

GAA tier enables very flexible and dynamic short-term use of spectrum with a very low entry barrier, but the GAA spectrum access and quality are less certain than with the exclusive LSA

Requires new near real-time sensing capabilities

Initially specific to US federal use, additional adaptability is needed for other regions

and big data and spectrum analytics

needed for the GAA tier

**LSA SAS**

Spectral efficiency Less efficient use of spectrum More efficient use of spectrum

Complexity Less complex Very complex due to the spectrum sensing

static a priori information on the incumbents

long-term spectrum leasing in 2.3 GHz band. Future LSA evolution will include spectrum sensing to provide more dynamic

Minimal additions to the existing 3GPP

Database Centralized geo-location database based on

Use of spectrum Current version is a static framework for

use of spectrum

network ecosystem.

Adaptability Initially focused on Europe but easily adaptable to other regions

**Table 1.** Brief comparison of LSA and SAS concepts.

from both technical and business perspectives are given in [43, 44].

62 Cognitive Radio in 4G/5G Wireless Communication Systems

IMT systems [47].

Software and hardware

The interest in private LTE and 5G networks has grown due to the increased number of IMT frequency bands, higher frequency ranges, variety in spectrum assignments for 3GPP technologies, and revolution of wireless industrial communication [51]. The feasibility study [52] addresses these issues and applies learning from the later developed SAS/CBRS system at the same time. The study considers, for example, how to provide temporary spectrum access for local high-quality wireless networks.

The new concepts for LSA evolution include short-term license periods, possibility to allocate spectrum locally, and supporting co-primary sharing, which can guarantee the quality of service from spectrum perspective [49, 53]. Most LTE use is static, when the spectrum assignments are considered. Even if the user equipments (UEs) are mobile, the spectrum use is more determined by the eNodeBs. They traditionally require masts, electricity, backhaul connectivity, and professional installers.

The temporary LTE or 5G spectrum access is most likely to be related to PMSE, Public Protection and Disaster Relief (PPDR), or Test and Development (T&D) licenses. The current mobile networks are wide area networks even if they are built for capacity. Most private LTE and 5G networks are local. PMSE, PPDR, and T&D networks are both temporary and local and thus can benefit most from LSA evolution.

## **4. LSA evolution systvem for 5G PMSE use case trial**

This section presents a trial of LSA evolution system developed for 5G PMSE use case. The trial focuses on utilizing LSA for sharing spectrum in 2.3–2.4 GHz band between wireless cameras (PMSE) and mobile network operator (MNO) serving users. When the spectrum is required by the incumbents, such as wireless video cameras during a sports event, the transmissions of the mobile network in this area need to be controlled to allow the operation of the wireless cameras in the band. The mobile network base stations on this band can be shut down or their transmission powers and potentially operating frequencies controlled.

The developed LSA evolution system allows to set priorities for different users of the spectrum, and thus it is possible, for example, to give the highest priority to the old/proprietary PMSE systems which cannot communicate bidirectionally with the spectrum manager, which includes the functionalities of LSA Controller and LSA Repository. The LTE/5G-based equipment can be controlled (their transmission frequencies and power levels adjusted) so that no interference is caused toward the old/proprietary PMSE equipment or other LTE/5G-based equipment.

The trial assumes that the broadcasters and other PMSE stakeholders may have a mixture of proprietary and LTE/5G PMSE wireless technology in use in the future. This trial demonstrates how broadcasters can gradually move from proprietary 2.3 GHz wireless camera technology to 2.3 GHz LTE/5G PMSE. Both old and new equipment can be used simultaneously within the trial system. One major advantage of LTE/5G radio-based PMSE is that the spectrum manager can directly control the equipment (e.g., change its operating frequency to avoid interference). Another advantage of having an own PMSE LTE/5G system compared to using commercial LTE/5G networks for the PMSE traffic is that the PMSE stakeholder is able to fully control the use and thus the load of its own PMSE system.

Both PMSE LTE and MNO LTE systems have a direct machine-to-machine (M2M) interface between the radio equipment and the spectrum manager. The priority order considered in the trial is as follows, from highest to lowest: PMSE, PMSE LTE, and MNO LTE. When the priority user changes the configuration of the LTE network, a notification about the change is automatically received in the spectrum manager. The spectrum manager processes the changed spectrum situation and evaluates if the lower priority use may cause harmful interference to the higher priority use. If there is a risk of interference, the spectrum manager evaluates which changes would be required to accommodate the higher priority use and to maintain the best

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On the high level, interference mitigation is implemented so that if there are frequency channels available, the lower priority use is transferred to those channels. If there are no other channels available, the power level of the secondary user is reduced or the transmission is denied. In this demonstration, the higher priority user is able to select the frequency channel to be used. An option for this could be that the higher priority user has the right to the spectrum resource in

The main target of the performed trial is to demonstrate the LSA evolution functions developed to the spectrum manager to enable dynamic spectrum sharing between users with dif-

**1.** MNO LTE1 (700 MHz) and MNO LTE2 (2.3 GHz) serving users (web surfing, video

**2.** PMSE LTE (2.3 GHz) turns on as a rapidly deployable network for PMSE, and spectrum is

**3.** PMSE user registers to the spectrum manager registration system, on the frequency cur-

**4.** MNO LTE2 limits its transmission power (if necessary) to follow interference limits, and

Corresponding snapshots of the 2.3 GHz spectrum band are illustrated in **Figure 4**. First, the lowest priority LTE service, such as SDL of MNO LTE2, operates in the band. Then, PMSE using rapidly deployable LTE air interface (PMSE LTE) requests for spectrum. At the same time, there is enough free spectrum for both to operate. Then, the proprietary PMSE equipment requests for spectrum, and the spectrum manager allocates suitable frequencies and power levels for all users. If necessary, MNO LTE2 adjusts the transmission power according to regulated interference limits to allow for the operation of higher priority users. Also, PMSE LTE that is controlled by the spectrum manager via M2M interface switches frequency (e.g., due to the limitations of proprietary PMSE equipment tuning range). Finally, all three net-

works operate on the shared spectrum without causing interference to each other.

the users remain connected to at least B28 (700 MHz) base station.

the band, but the specific frequency channel is determined by the spectrum manager.

possible service level also for the lower priority use.

ferent levels of priority. The steps performed in the trial were:

available for both MNO LTE2 and PMSE LTE.

**5.** PMSE LTE changes channel to give space to PMSE.

**6.** Proprietary PMSE equipment turns on.

rently in use for PMSE LTE.

streaming).

The architecture of the trial setup shown in **Figure 3** consists of PMSE equipment operating occasionally on 2.3 GHz band and MNO LTE network operating on 700 and 2.3 GHz bands. This represents a situation where MNO employs additional capacity on 2.3 GHz band using, for example, supplemental downlink (SDL) concept. Proprietary PMSE equipment represents an OFDM-based proprietary solution for wireless cameras operating on the band. PMSE LTE in **Figure 3** is a rapidly deployable LTE/5G network for PMSE purposes. Commercial base stations and LTE terminals were used in the trial. The proprietary PMSE equipment was emulated in the trial with a DVB-T/DVB-T2 transmission and Samsung S8 phones streaming video served as LTE-based PMSE equipment.

Spectrum manager orchestrates the operation of the different systems on 2.3 GHz shared band. PMSE system information is collected with a web-based reservation system, where the users of the devices can make reservations for their intended use. The reservation system has been piloted in the Netherlands in 2017–2018 [34]. The control of the PMSE devices also takes place through the reservation system so that the user of the devices is informed about the required spectrum use changes and the user has to deploy the configuration changes in their devices.

**Figure 3.** Spectrum demonstration architecture.

Both PMSE LTE and MNO LTE systems have a direct machine-to-machine (M2M) interface between the radio equipment and the spectrum manager. The priority order considered in the trial is as follows, from highest to lowest: PMSE, PMSE LTE, and MNO LTE. When the priority user changes the configuration of the LTE network, a notification about the change is automatically received in the spectrum manager. The spectrum manager processes the changed spectrum situation and evaluates if the lower priority use may cause harmful interference to the higher priority use. If there is a risk of interference, the spectrum manager evaluates which changes would be required to accommodate the higher priority use and to maintain the best possible service level also for the lower priority use.

On the high level, interference mitigation is implemented so that if there are frequency channels available, the lower priority use is transferred to those channels. If there are no other channels available, the power level of the secondary user is reduced or the transmission is denied. In this demonstration, the higher priority user is able to select the frequency channel to be used. An option for this could be that the higher priority user has the right to the spectrum resource in the band, but the specific frequency channel is determined by the spectrum manager.

The main target of the performed trial is to demonstrate the LSA evolution functions developed to the spectrum manager to enable dynamic spectrum sharing between users with different levels of priority. The steps performed in the trial were:

**Figure 3.** Spectrum demonstration architecture.

served as LTE-based PMSE equipment.

64 Cognitive Radio in 4G/5G Wireless Communication Systems

The trial assumes that the broadcasters and other PMSE stakeholders may have a mixture of proprietary and LTE/5G PMSE wireless technology in use in the future. This trial demonstrates how broadcasters can gradually move from proprietary 2.3 GHz wireless camera technology to 2.3 GHz LTE/5G PMSE. Both old and new equipment can be used simultaneously within the trial system. One major advantage of LTE/5G radio-based PMSE is that the spectrum manager can directly control the equipment (e.g., change its operating frequency to avoid interference). Another advantage of having an own PMSE LTE/5G system compared to using commercial LTE/5G networks for the PMSE traffic is that the PMSE stakeholder is able

The architecture of the trial setup shown in **Figure 3** consists of PMSE equipment operating occasionally on 2.3 GHz band and MNO LTE network operating on 700 and 2.3 GHz bands. This represents a situation where MNO employs additional capacity on 2.3 GHz band using, for example, supplemental downlink (SDL) concept. Proprietary PMSE equipment represents an OFDM-based proprietary solution for wireless cameras operating on the band. PMSE LTE in **Figure 3** is a rapidly deployable LTE/5G network for PMSE purposes. Commercial base stations and LTE terminals were used in the trial. The proprietary PMSE equipment was emulated in the trial with a DVB-T/DVB-T2 transmission and Samsung S8 phones streaming video

Spectrum manager orchestrates the operation of the different systems on 2.3 GHz shared band. PMSE system information is collected with a web-based reservation system, where the users of the devices can make reservations for their intended use. The reservation system has been piloted in the Netherlands in 2017–2018 [34]. The control of the PMSE devices also takes place through the reservation system so that the user of the devices is informed about the required spectrum use changes and the user has to deploy the configuration changes in their devices.

to fully control the use and thus the load of its own PMSE system.

Corresponding snapshots of the 2.3 GHz spectrum band are illustrated in **Figure 4**. First, the lowest priority LTE service, such as SDL of MNO LTE2, operates in the band. Then, PMSE using rapidly deployable LTE air interface (PMSE LTE) requests for spectrum. At the same time, there is enough free spectrum for both to operate. Then, the proprietary PMSE equipment requests for spectrum, and the spectrum manager allocates suitable frequencies and power levels for all users. If necessary, MNO LTE2 adjusts the transmission power according to regulated interference limits to allow for the operation of higher priority users. Also, PMSE LTE that is controlled by the spectrum manager via M2M interface switches frequency (e.g., due to the limitations of proprietary PMSE equipment tuning range). Finally, all three networks operate on the shared spectrum without causing interference to each other.

When the LSA users have a primary status and when they are protected from interference,

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When the 5G massive Machine-Type Communication (mMTC) is deployed in coverage networks, LSA may not be the best solution, as the spectrum sharing in the coverage bands is not as beneficial as in the capacity bands. On the other hand, many of the sub-GHz wide-area IoT networks operate on license-exempt bands, which cannot guarantee quality of service either. The LSA spectrum sharing does not change mobility or handovers in the mobile networks compared to non-LSA mobile networks. The main issue in this respect is the graceful shutdown. The sharing agreement may allow a reasonable delay, i.e., several minutes or more, between the moment information that the primary user requiring interference protection arrives and the moment when the interference protection has to be carried out in the LSA system. In that case, the operations, administration, and maintenance (OAM) of the mobile network can force the mobile to non-LSA bands before the LSA system deploys the interference protection in the LSA band. The graceful shutdown is not a part of the LSA system but

This chapter has discussed why spectrum sharing is needed and introduced the LSA concept developed to provide a predictable QoS and exclusive access to shared spectrum resources. The first phase of LSA development and standardization created a somewhat static system and rules for the use of LSA in the 2.3–2.4 GHz frequency band. This version of LSA is best suited to facilitate access to sub-6 GHz frequency bands where the existing incumbents are

However, the novel use cases in 5G require a more dynamic access to the spectrum and novel solutions for coordinated control and spectrum management. Spectrum sensing techniques are needed to provide the more dynamic access to spectrum, as the current version of LSA and its static spectrum allocations are insufficient for this. The spectrum sensing techniques however still need to evolve to be able to guarantee protection from harmful interference.

The development of LSA evolution is underway, and the other new concepts needed for LSA evolution include short-term license periods, possibility to allocate spectrum locally, and support for co-primary sharing, which can guarantee the QoS from spectrum perspective. The chapter also described a demonstration of LSA evolution system with spectrum user

This work is supported by Business Finland under the project Critical Operations over Regular Networks (CORNET). The views expressed in this contribution are those of the authors and

LSA can be used also for 5G Ultrareliable Low-Latency Communication (URLLC).

rather a feature of the OAM.

**Acknowledgements**

do not necessarily represent the projects.

not efficiently using their spectral resources.

prioritization, which was created for 5G PMSE use case.

**6. Conclusion**

**Figure 4.** 2.3 GHz spectrum use corresponding to trial steps.

## **5. LSA evolution in 5G networks**

A mobile service of a mobile network operator consists of different mobile network technologies, like GSM, WCDMA, LTE, and 5G. Each of the technologies, especially LTE, has several frequency bands. The bands below 1 GHz are coverage bands, and the bands above 2 GHz are capacity bands. The capacity bands are available only in densely populated areas.

Mobile phones primarily make the decision which technology and which band(s) they use. The availability of the LSA secondary bands cannot be guaranteed at any time or location, but the situation does not differ much from the availability of the capacity bands, when considering the availability of the bands from the mobile device perspective. At an arbitrary location and time, only a part of the deployed technologies and frequency bands are available for a specific mobile device. The generic secondary LSA spectrum use fits best to 5G enhanced Mobile Broadband (eMBB).

The original LSA was developed to allow mobile as a secondary user on the bands, which have other types of priority users. The recent development in ETSI RRS considers LSA for local high-quality networks. The main issue to ensure a guarantee of quality is to have a sharing agreement, where the LSA user is the primary user and is protected from interference. When the LSA users have a primary status and when they are protected from interference, LSA can be used also for 5G Ultrareliable Low-Latency Communication (URLLC).

When the 5G massive Machine-Type Communication (mMTC) is deployed in coverage networks, LSA may not be the best solution, as the spectrum sharing in the coverage bands is not as beneficial as in the capacity bands. On the other hand, many of the sub-GHz wide-area IoT networks operate on license-exempt bands, which cannot guarantee quality of service either.

The LSA spectrum sharing does not change mobility or handovers in the mobile networks compared to non-LSA mobile networks. The main issue in this respect is the graceful shutdown. The sharing agreement may allow a reasonable delay, i.e., several minutes or more, between the moment information that the primary user requiring interference protection arrives and the moment when the interference protection has to be carried out in the LSA system. In that case, the operations, administration, and maintenance (OAM) of the mobile network can force the mobile to non-LSA bands before the LSA system deploys the interference protection in the LSA band. The graceful shutdown is not a part of the LSA system but rather a feature of the OAM.

## **6. Conclusion**

**5. LSA evolution in 5G networks**

**Figure 4.** 2.3 GHz spectrum use corresponding to trial steps.

66 Cognitive Radio in 4G/5G Wireless Communication Systems

Mobile Broadband (eMBB).

A mobile service of a mobile network operator consists of different mobile network technologies, like GSM, WCDMA, LTE, and 5G. Each of the technologies, especially LTE, has several frequency bands. The bands below 1 GHz are coverage bands, and the bands above 2 GHz are

Mobile phones primarily make the decision which technology and which band(s) they use. The availability of the LSA secondary bands cannot be guaranteed at any time or location, but the situation does not differ much from the availability of the capacity bands, when considering the availability of the bands from the mobile device perspective. At an arbitrary location and time, only a part of the deployed technologies and frequency bands are available for a specific mobile device. The generic secondary LSA spectrum use fits best to 5G enhanced

The original LSA was developed to allow mobile as a secondary user on the bands, which have other types of priority users. The recent development in ETSI RRS considers LSA for local high-quality networks. The main issue to ensure a guarantee of quality is to have a sharing agreement, where the LSA user is the primary user and is protected from interference.

capacity bands. The capacity bands are available only in densely populated areas.

This chapter has discussed why spectrum sharing is needed and introduced the LSA concept developed to provide a predictable QoS and exclusive access to shared spectrum resources. The first phase of LSA development and standardization created a somewhat static system and rules for the use of LSA in the 2.3–2.4 GHz frequency band. This version of LSA is best suited to facilitate access to sub-6 GHz frequency bands where the existing incumbents are not efficiently using their spectral resources.

However, the novel use cases in 5G require a more dynamic access to the spectrum and novel solutions for coordinated control and spectrum management. Spectrum sensing techniques are needed to provide the more dynamic access to spectrum, as the current version of LSA and its static spectrum allocations are insufficient for this. The spectrum sensing techniques however still need to evolve to be able to guarantee protection from harmful interference.

The development of LSA evolution is underway, and the other new concepts needed for LSA evolution include short-term license periods, possibility to allocate spectrum locally, and support for co-primary sharing, which can guarantee the QoS from spectrum perspective. The chapter also described a demonstration of LSA evolution system with spectrum user prioritization, which was created for 5G PMSE use case.

## **Acknowledgements**

This work is supported by Business Finland under the project Critical Operations over Regular Networks (CORNET). The views expressed in this contribution are those of the authors and do not necessarily represent the projects.

## **Author details**

Juha Kalliovaara1 , Tero Jokela1 , Heikki Kokkinen2 and Jarkko Paavola1 \*

## **References**

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Juha Kalliovaara1

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2 Fairspectrum, Espoo, Finland

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, Tero Jokela1

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\*Address all correspondence to<EMAIL_ADDRESS>
1 Turku University of Applied Sciences, Turku, Finland

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[48] Höyhtyä M, Mammela A, Eskola M, Matinmikko M, Kalliovaara J, Ojaniemi J, Suutala J, Ekman R, Bacchus R, Roberson D. Spectrum occupancy measurements: A survey and use of interference maps. IEEE Communications Surveys & Tutorials;**18**(4):2386-2414, Fourthquarter 2016. DOI: 10.1109/COMST.2016.2559525

**Chapter 4**

**Provisional chapter**

**Spectrum Decision Framework to Support Cognitive**

**Spectrum Decision Framework to Support Cognitive**

RF Spectrum Decision in Cognitive Radio enables unlicensed users of wireless communication systems to occupy the vacant spectrum slots as a solution to scarce spectrum. Internet of Things (IoT) is a wide-reaching network of unified entities. IoT capable things will be interconnected through wireless communication technologies offering cost-effectiveness and accessibility to remote users making quality life style. IoT implementation suffers from challenges of vulnerabilities to dynamic environmental conditions, ease of access, bandwidth allocation and utilization, and cost to purchase RF spectrum. As RF spectrum is a precious commodity and there is a dearth of RF spectrum, hence IoT connections are drifting towards Cognitive Radio Networks (CRNs). Permeating things with cognitive abilities will be able to make RF spectrum decisions to achieve interference-free and wireless connectivity as per their QoS requirements. The wireless systems are rapidly advancing. The leap from packet switching along with circuit switching with 144 kbps data rate (2G and 2.5G) to Long Term Evolution Advanced (LTE-A), i.e., 4G occurred in one decade time frame. As the current wireless connectivity is aimed at higher capacity, higher data rate, low end-to-end latency, massive device connectivity, reduced cost and consistent Quality of Experience (QoE) provision, therefore, 4G is being replaced with 5G. Presently the Radio Frequency (RF) spectrum band is fully sold out and allocated to various wireless operators and applications. On the other hand, new wireless applications are emerging and there is a serious dearth of frequency spectrum to be allocated to emerging wireless services. The efficient utilization of assigned RF spectrum which is otherwise underutilized due to the typical usage by the licensed users known as Primary Users (PUs) is the one of the best possible way to implement IoT in 5G. Thus the Spectrum Decision by unlicensed users of CR holds a significance in CR-based IoT in 5G and beyond network. This chapter describes a scientific supported spectrum decision support framework for CR Network. The main goal of this chapter is to discuss how CR technology can be helpful for

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

DOI: 10.5772/intechopen.80991

**Radio Based IoT in 5G**

**Radio Based IoT in 5G**

Adil Masood Siddique

Adil Masood Siddique

**Abstract**

the IoT paradigm.

Ahmad Naeem Akhtar, Fahim Arif and

Ahmad Naeem Akhtar, Fahim Arif and

http://dx.doi.org/10.5772/intechopen.80991

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

#### **Spectrum Decision Framework to Support Cognitive Radio Based IoT in 5G Spectrum Decision Framework to Support Cognitive Radio Based IoT in 5G**

DOI: 10.5772/intechopen.80991

Ahmad Naeem Akhtar, Fahim Arif and Adil Masood Siddique Ahmad Naeem Akhtar, Fahim Arif and Adil Masood Siddique

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.80991

#### **Abstract**

[48] Höyhtyä M, Mammela A, Eskola M, Matinmikko M, Kalliovaara J, Ojaniemi J, Suutala J, Ekman R, Bacchus R, Roberson D. Spectrum occupancy measurements: A survey and use of interference maps. IEEE Communications Surveys & Tutorials;**18**(4):2386-2414,

[49] Yrjölä S, Kokkinen H. Licensed shared access evolution enables early access to 5G spectrum and novel use cases. EAI Endorsed Transactions on Wireless Spectrum. Dec

[50] Frascolla V, et al. Dynamic licensed shared access—A new architecture and spectrum allocation techniques. In: 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall);

[51] Kokkinen H, Yrjölä S, Engelberg J, Kokkinen T. Pricing private LTE and 5G radio licenses on 3.5 GHz. Cognitive Radio Oriented Wireless Networks and Communications

[52] ETSI. ETSI TR 103 588 V1.1.1: Reconfigurable radio systems (RRS); Feasibility study on temporary spectrum access for local high-quality wireless networks. Feb 2018

[53] Guirao MDP, Wilzeck A, Schmidt A, Septinus K, Thein C. Locally and temporary shared spectrum as opportunity for vertical sectors in 5G. IEEE Network. Nov 2017;**31**(6):24-31.

Fourthquarter 2016. DOI: 10.1109/COMST.2016.2559525

2017;**3**(10)**,**e1:1-11. DOI: 10.4108/eai.12-12-2017.153463

(CROWNCOM). 2018

DOI: 10.1109/MNET.2017.1700079

72 Cognitive Radio in 4G/5G Wireless Communication Systems

Montreal, QC. 2016. pp. 1-5. DOI: 10.1109/VTCFall.2016.7880971

RF Spectrum Decision in Cognitive Radio enables unlicensed users of wireless communication systems to occupy the vacant spectrum slots as a solution to scarce spectrum. Internet of Things (IoT) is a wide-reaching network of unified entities. IoT capable things will be interconnected through wireless communication technologies offering cost-effectiveness and accessibility to remote users making quality life style. IoT implementation suffers from challenges of vulnerabilities to dynamic environmental conditions, ease of access, bandwidth allocation and utilization, and cost to purchase RF spectrum. As RF spectrum is a precious commodity and there is a dearth of RF spectrum, hence IoT connections are drifting towards Cognitive Radio Networks (CRNs). Permeating things with cognitive abilities will be able to make RF spectrum decisions to achieve interference-free and wireless connectivity as per their QoS requirements. The wireless systems are rapidly advancing. The leap from packet switching along with circuit switching with 144 kbps data rate (2G and 2.5G) to Long Term Evolution Advanced (LTE-A), i.e., 4G occurred in one decade time frame. As the current wireless connectivity is aimed at higher capacity, higher data rate, low end-to-end latency, massive device connectivity, reduced cost and consistent Quality of Experience (QoE) provision, therefore, 4G is being replaced with 5G. Presently the Radio Frequency (RF) spectrum band is fully sold out and allocated to various wireless operators and applications. On the other hand, new wireless applications are emerging and there is a serious dearth of frequency spectrum to be allocated to emerging wireless services. The efficient utilization of assigned RF spectrum which is otherwise underutilized due to the typical usage by the licensed users known as Primary Users (PUs) is the one of the best possible way to implement IoT in 5G. Thus the Spectrum Decision by unlicensed users of CR holds a significance in CR-based IoT in 5G and beyond network. This chapter describes a scientific supported spectrum decision support framework for CR Network. The main goal of this chapter is to discuss how CR technology can be helpful for the IoT paradigm.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Keywords:** cognitive radio, internet of things, 5G/B5G, Spectrum decision framework, primary user, IoT-User

All previously mentioned research contributions are more concept oriented for IoT in 5G network. The RF spectrum accessibility as per the wireless application for the user in IoT environment remains an open research area. Motivated by this, a comprehensive survey on 5G networks embedded with IoT applications based on CR ensuring A6 connectivity by accessing across the entire RF spectrum has been carried out in this chapter. A case study based on this survey for CR-based IoT in 5G networks has also been proposed to validate

Spectrum Decision Framework to Support Cognitive Radio Based IoT in 5G

http://dx.doi.org/10.5772/intechopen.80991

75

Introduction, related work and the motivation of the work is given in the first section. Evolution to 5G is given in Section 3. IoT in 5G network is described in Section 4. Section 5 gives an account for 5G with CRN based IoT and the need for RF spectrum management is given in Section 6. Section 7 concludes the chapter. **Table 1** given below lists the abbreviations

**Abbreviation Definition Abbreviation Definition**

BTS Base Transceiver Station MHz Mega hertz CCC Common control channel MVR Majority vote rule CDMA Code Division Multiplexing Access NB Narrow band Narrow band CR Cognitive Radio NI National instrument

AWGN Additive White Gaussian Noise MF Membership function

AMPS Advanced Mobile Phone Service MIMO O Multiple input multiple output B3G, B4G, B5G Beyond third, fourth and fifth generations MISO Multiple input single output

CRN Cognitive Radio Networks OFDM Orthogonal frequency division

ED Energy Detection QPSK Quadrature phase shift keying

FSDM Frequency Spectrum Decision Mechanism ROC Receiver operating characteristics

RFID Radio frequency identification SDSF Spectrum decision support

DSA Dynamic Spectrum Access PC Personal Computer

DVB Digital Video Chat Broadcasting PU Primary user D2D Device to Device QoE Quality of experience IoT Internet of Things QoS Quality of service

multiplexing

RASC Random channel assignment with single channel

RFID Radio frequency identification

framework

SDR Software define radio

PDP Poisson distribution process

PSD Power spectral density

the concept.

used in this chapter.

**2. Structure of the chapter**

DSMF Dynamic Spectrum Management Framework

EV-DO Evolution Data Only/Evolution Data Optimized

ETACS European Total Access Communication

HART Highway Addressable Remote Transducer

System

Protocol

## **1. Introduction**

4G provides voice, data and multimedia imparting to the wireless subscribers on every time and everywhere basis at higher data rates in Multimedia Messaging Service, Digital Video Chat Broadcasting (DVB), video chat, High Definitive TV content and mobile TV. As an application of 5G, the wireless systems are deployed to make All the people and things to be connected Any time with Anyone while being Anywhere via Any Path or Network and Any service (A6 connection). This A6 connection is known as Internet of Things (IoT). Internet of Things (IoT) is the environment where all over smart interconnected objects are connected with each other through unique addressing schemes based on specific telecommunication standards and protocols [1]. IoT based devices are to be interconnected through Base Transceiver Stations (BTSs) in wireless operations and the BTSs are linked with backhaul connectivity through Optical Fiber Transmission systems achieving higher bandwidths supplemented by Terrestrial Microwave links. The wireless Radio Frequency spectrum (WRFS) is almost completely assigned to existing wireless applications. At the same time, WRFS is underutilized due to the typical usage of mobile and other wireless services. To address this problem, Cognitive Radio (CR) has emerged as an enabling technology which offers a solution to spectrum scarcity problem. Hence, the CR-based IoT system has by default becomes a focus for researchers in wireless communication systems. CRN systems have emerged as a capable solution to the spectrum scarcity and as an enabling technology for the optimum utilization of otherwise underutilized RF spectrum, humanizing the synchronicity and interoperability in various wireless and mobile communications systems transforming into telecommunication devices and systems autonomous and self-reconfigurable. With swift shift to smart communication technologies and infrastructure, the Internet of Things (IoT) has emerged as a modern challenge in international Telecommunication industry and wireless applications. SUs access RF spectrum bands in heterogeneous manners in CRN and IoT supported smart area consists of heterogeneous devices, which are mobile as well as static in nature [1]. At the same time, the next generation mobile communication network referred to as the Fifth Generation (5G) is almost realized in the advanced telecommunication era [2]. 5G and beyond is expected to integrate the contemporary wireless technologies into an all Internet Protocol (IP) based networks which offer high performance worldwide network [3]. As the bandwidth for 5G and beyond is very large and the WRFS offers a large number of non-continuous idle spectrum slots in 5G communication as well [4], there is a requirement to identify the unused spectrum slots not being used by respective licensed users called primary users (PUs). This process is known as Spectrum Sensing (SS) in Cognitive Radio systems. Accurate SS allows the secondary users (SUs) to opportunistically use the vacant spectrum slots as per their wireless applications and vacate when the PU arrives in the network. This process is termed as spectrum decision. When optimally done, the SU along with PU will be enabled users using IoT paradigm in 5G/B5G networks. Therefore, spectrum decision is an important parameter for the deployment of CR-based IoT in 5G/B5G network.

All previously mentioned research contributions are more concept oriented for IoT in 5G network. The RF spectrum accessibility as per the wireless application for the user in IoT environment remains an open research area. Motivated by this, a comprehensive survey on 5G networks embedded with IoT applications based on CR ensuring A6 connectivity by accessing across the entire RF spectrum has been carried out in this chapter. A case study based on this survey for CR-based IoT in 5G networks has also been proposed to validate the concept.

## **2. Structure of the chapter**

**Keywords:** cognitive radio, internet of things, 5G/B5G, Spectrum decision framework,

4G provides voice, data and multimedia imparting to the wireless subscribers on every time and everywhere basis at higher data rates in Multimedia Messaging Service, Digital Video Chat Broadcasting (DVB), video chat, High Definitive TV content and mobile TV. As an application of 5G, the wireless systems are deployed to make All the people and things to be connected Any time with Anyone while being Anywhere via Any Path or Network and Any service (A6 connection). This A6 connection is known as Internet of Things (IoT). Internet of Things (IoT) is the environment where all over smart interconnected objects are connected with each other through unique addressing schemes based on specific telecommunication standards and protocols [1]. IoT based devices are to be interconnected through Base Transceiver Stations (BTSs) in wireless operations and the BTSs are linked with backhaul connectivity through Optical Fiber Transmission systems achieving higher bandwidths supplemented by Terrestrial Microwave links. The wireless Radio Frequency spectrum (WRFS) is almost completely assigned to existing wireless applications. At the same time, WRFS is underutilized due to the typical usage of mobile and other wireless services. To address this problem, Cognitive Radio (CR) has emerged as an enabling technology which offers a solution to spectrum scarcity problem. Hence, the CR-based IoT system has by default becomes a focus for researchers in wireless communication systems. CRN systems have emerged as a capable solution to the spectrum scarcity and as an enabling technology for the optimum utilization of otherwise underutilized RF spectrum, humanizing the synchronicity and interoperability in various wireless and mobile communications systems transforming into telecommunication devices and systems autonomous and self-reconfigurable. With swift shift to smart communication technologies and infrastructure, the Internet of Things (IoT) has emerged as a modern challenge in international Telecommunication industry and wireless applications. SUs access RF spectrum bands in heterogeneous manners in CRN and IoT supported smart area consists of heterogeneous devices, which are mobile as well as static in nature [1]. At the same time, the next generation mobile communication network referred to as the Fifth Generation (5G) is almost realized in the advanced telecommunication era [2]. 5G and beyond is expected to integrate the contemporary wireless technologies into an all Internet Protocol (IP) based networks which offer high performance worldwide network [3]. As the bandwidth for 5G and beyond is very large and the WRFS offers a large number of non-continuous idle spectrum slots in 5G communication as well [4], there is a requirement to identify the unused spectrum slots not being used by respective licensed users called primary users (PUs). This process is known as Spectrum Sensing (SS) in Cognitive Radio systems. Accurate SS allows the secondary users (SUs) to opportunistically use the vacant spectrum slots as per their wireless applications and vacate when the PU arrives in the network. This process is termed as spectrum decision. When optimally done, the SU along with PU will be enabled users using IoT paradigm in 5G/B5G networks. Therefore, spectrum decision is an important parameter for the deployment of CR-based IoT in

primary user, IoT-User

74 Cognitive Radio in 4G/5G Wireless Communication Systems

**1. Introduction**

5G/B5G network.

Introduction, related work and the motivation of the work is given in the first section. Evolution to 5G is given in Section 3. IoT in 5G network is described in Section 4. Section 5 gives an account for 5G with CRN based IoT and the need for RF spectrum management is given in Section 6. Section 7 concludes the chapter. **Table 1** given below lists the abbreviations used in this chapter.

**Table 1.** Abbreviations used.

## **3. Advancement of wireless technologies from 1G to 5G**

Since the inception of the first generation (1G) cellular systems in telecommunication system, the entire pattern of living environment including the people's work, lifestyle and the agricultural and industrial development trends has been effected. Evolution to the fifth generation (5G) is the progressive advancement in the telecommunication industry to keep with the growing pace of mobile data traffic, huge volume of device connections and continuous emergence of latest commercial scenarios. Over the last one decade or so, the wireless communications have the capability to connect all the existing mobile technologies, to build a terminal that is to support the voice, video and data applications with respective QoS requirements guaranteed i.e., at very high data rates and users speeds making it a 5G/B5G environment. The chronological evolution to 5G/B5G [5] is listed here in the **Table 2**.

Operation Megacommunications concept), with guaranteed QoS requirements of wireless services [7]. Spectrum decision in CR would ensure spectrum scarcity problems and IoT complies wireless A6 connections for users, making CR-based IoT in 5G networks with a focus on spectrum decision framework in CR for IoT in 5G networks, an interesting study for researchers.

**Applications Standards Data rates Mobility**

NMT, AMPS, TACS, ETACS and JTACS

TDMA, GSM, CDMA, 2.4 GHz narrowband

CDMA2000, EV-DO, W-CDMA, 802.11 PAN, Bluetooth.

DAB/DVB, cellular GSM, IMT-2000, WLAN, IR, UWB, DSL, LTE-A, IEEE802.16e

WLAN

3GPP, LTE

5G/B5G 5th Generation and beyond. 4G + WISDOM 2015

**offered**

http://dx.doi.org/10.5772/intechopen.80991

14.4 kbps Low Speed 1995–1997

medium speeds

and High Speed

Speed

Speed

144 kbps Low and

Spectrum Decision Framework to Support Cognitive Radio Based IoT in 5G

384 kbps Medium

<50 Mbps High

<100 Mbps Very High

**Time span**

77

1997–2000

2000–2005

2005–2010

2010 onwards

onwards

Mobile data and IoT are the future internet for everything and will be the key and motivating force in the advancement of 5G/B5G networks. In the time to come, likely by 2021–2025, 5G/ B5G will not only meet the assorted requirements of people in various constituencies of daily life such as residence, work, leisure, and transportation, but also will infuse the IoT and light up the diverse specialized domains to the professional aspects of human life and the industry such as medical sciences and facilities and transportation to realize the true interconnectedness of all things [9]. The realization of IoT is dependent on internet application scenario based requirements which converge to 5G networks and are not guaranteed in 4G and LTE

**4. IoT in 5G/B5G networks**

**Wireless technology generation**

3G (IMT 2000)

1G (Analog) 1st Generation of the mobile

service.

2G (Digital) 2nd Generation of wireless telephone technology introducing a data service; SMS (short message service)

4G 4Th Generation of mobile telecommunications

telecommunication technology standardized by the voice

3rd Generation of mobile telecommunications (International Mobile Telecommunications-2000)

B3G Beyond 3rd Generation WiBro802.16e, WiMax,

**Table 2.** Progressive evolution of Mobile services from 1G to 5G [5].

technologies. These requirements are listed in **Table 3**.

Device to device (D2D) communications in 3GPP and LTE standards offers transfer of data directly to each other without the involvement of BSs [6]. This reduces the workload and energy consumption of BS thereby offering a good platform for 5G. Emerging 5G wireless communications envision very high data rates (typically of Gbps order), extremely low latency, significant increase in BTS capacity and improvement in PUs' and SUs' perceived Quality of Experience (QoE), compared to existing 4G/3G wireless networks. The 5G/B5G implies the whole wireless world interconnection (WISDOM; Wireless Innovative System for Dynamic

**Table 2.** Progressive evolution of Mobile services from 1G to 5G [5].

Operation Megacommunications concept), with guaranteed QoS requirements of wireless services [7]. Spectrum decision in CR would ensure spectrum scarcity problems and IoT complies wireless A6 connections for users, making CR-based IoT in 5G networks with a focus on spectrum decision framework in CR for IoT in 5G networks, an interesting study for researchers.

## **4. IoT in 5G/B5G networks**

**3. Advancement of wireless technologies from 1G to 5G**

**Abbreviation Definition Abbreviation Definition** ROC Receiver operating characteristics SG Smart grid

6TiSCH IP (IPv6 settings) integrated with Time

76 Cognitive Radio in 4G/5G Wireless Communication Systems

URLLC Ultra-reliable low latency communication

WCDMA Wide-band code division multiple access

WiMAX Worldwide interoperability for microwave

SNR Signal to noise ratio SU Secondary user

UE User equipment

UWB Ultra-wideband

WiFi Wider Fidelity

**Table 1.** Abbreviations used.

access WLAN Wireless local area networks WWRF Wireless world research forum

TDMA Time division multiple access

synchronized channel hopping

The chronological evolution to 5G/B5G [5] is listed here in the **Table 2**.

Since the inception of the first generation (1G) cellular systems in telecommunication system, the entire pattern of living environment including the people's work, lifestyle and the agricultural and industrial development trends has been effected. Evolution to the fifth generation (5G) is the progressive advancement in the telecommunication industry to keep with the growing pace of mobile data traffic, huge volume of device connections and continuous emergence of latest commercial scenarios. Over the last one decade or so, the wireless communications have the capability to connect all the existing mobile technologies, to build a terminal that is to support the voice, video and data applications with respective QoS requirements guaranteed i.e., at very high data rates and users speeds making it a 5G/B5G environment.

Device to device (D2D) communications in 3GPP and LTE standards offers transfer of data directly to each other without the involvement of BSs [6]. This reduces the workload and energy consumption of BS thereby offering a good platform for 5G. Emerging 5G wireless communications envision very high data rates (typically of Gbps order), extremely low latency, significant increase in BTS capacity and improvement in PUs' and SUs' perceived Quality of Experience (QoE), compared to existing 4G/3G wireless networks. The 5G/B5G implies the whole wireless world interconnection (WISDOM; Wireless Innovative System for Dynamic Mobile data and IoT are the future internet for everything and will be the key and motivating force in the advancement of 5G/B5G networks. In the time to come, likely by 2021–2025, 5G/ B5G will not only meet the assorted requirements of people in various constituencies of daily life such as residence, work, leisure, and transportation, but also will infuse the IoT and light up the diverse specialized domains to the professional aspects of human life and the industry such as medical sciences and facilities and transportation to realize the true interconnectedness of all things [9]. The realization of IoT is dependent on internet application scenario based requirements which converge to 5G networks and are not guaranteed in 4G and LTE technologies. These requirements are listed in **Table 3**.

**Table 3.** Modern trends and requirements in IoT [8].

## **5. 5G/B5G with CRN based IoT**

The Internet of Things (IoT) envisions thousands of constrained devices with sensing, actuating, processing, and communication capabilities able to observe the world with an unprecedented resolution. According to Cisco, more than 50 billion devices are expected to be connected to the internet by 2020 and 20% of which are from the industry sector [9]. These connected things will generate huge volume of data that need to be analyzed to gain insight behind this big IoT data. Moreover, in the industrial environments (industry 4.0) as well in in smart spaces (building, houses, etc.) and connected cars communications often require high reliability, low latency and scalability. Several technologies such as BLE, Zigbee, Wireless HART, 6TiSCH, LPWAN (Lora, Sigfox, etc.) have been proposed to fit these requirements. The forthcoming 5G networks are promising not only by increased data rates but also lowlatency data communication for latency-critical IoT applications. 5G will enable massive IoT devices connected via a myriad of networks and critical machine type communications. While the massive IoT is more concerned about scalability deep coverage and energy efficiency, the later requires ultra-low latency and extreme reliability (URLLC). Recently, the fog-to-thing continuum [10] is proposed to mitigate the heavy burden on the network due to the centralized processing and storing of the massive IoT data. Fog-enabled IoT architectures ensure closer processing in proximity to the things, which results in small, deterministic latency that enables real time applications and enforced security. The IoT is a modern and the state of the art archetype in the technological advancement which is evolving as a future Internet. As per the principal vision of the IoT, the further requirement is the ubiquity of the Internet, after connecting people anytime and everywhere, is to connect extinct entities. By providing objects with embedded communication capabilities and a common addressing scheme, a distributed and permeating network of impeccably connected diverse electric and electronic devices is designed, which is to be indigenously cohesive into the existing Internet connections and mobile networks. Formally, IoT can be defined as, " A worldwide network on electronically interconnected devices uniquely addressable, based on standard communication protocols and allows users to be A6 connected" [11]. Thus allowing for the development of new intelligent services available anytime, anywhere, by anyone and anything. Latest research work and

technological systems are converging towards IoT and CRNs. Since the spectrum assignment policy involves expenditures for buying the RF spectrum, the assignment of spectrum for a huge number of devices and objects required for IoT connectivity will result in redundant cost effects. CRNs due to its typical spectrum utilization characteristic emerge in realization of IoT. The idea of a reserved spectrum slot as shared-to-reserve (SR) and reserved-to-share (RS) schemes in CR-HetNets proposed in [12] can enhance the system throughput and would offer a high bandwidth transmission for IoT-Us in CRN. A CRN properties enabling it for IoT

**Figure 1.** CRN system with its properties and research directions enabling it for IoT system for A6 connections by SUs.

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79

Usually, the SU operates in half-duplex mode (HD), i.e., it can either transmit or sense at any instant of time [13]. Due to this HD operation of SU, there is a possibility that harmful interference to PU is caused on unexpected arrival of PU and its activity during the transmission of SU. Hence the spectrum sensing should be a continuous process and SU must vacate the licensed channel on arrival of its PU and switch to another suitable channel as per its applica-

**6. Need for Spectrum management in IoT based 5G/B5G networks**

External storage solutions offer nearly unlimited capacity, with dedicated signal processing to sort through data and find signals, interactions or events of interest. These long-duration, high-bandwidth solutions are ideal for today's crowded spectrum and advanced technologies such as cognitive radios. WRFS is characterized by PU activity modeling and accurate SS [14]. This means that the spectrum management holds a great significant in CR technologies

applications is shown in **Figure 1**.

tion, i.e., a befitting spectrum decision framework is essential.

Spectrum Decision Framework to Support Cognitive Radio Based IoT in 5G http://dx.doi.org/10.5772/intechopen.80991 79

**Figure 1.** CRN system with its properties and research directions enabling it for IoT system for A6 connections by SUs.

**5. 5G/B5G with CRN based IoT**

**Table 3.** Modern trends and requirements in IoT [8].

**subscribers**

78 Cognitive Radio in 4G/5G Wireless Communication Systems

**coverage**

Requirements Seamless

**Wide and seamless**

connectivity with high speed service in mobility of the subscriber

**Mobile data provided internet to the**

**High capacity to guarantee QoS requirements for Internet Applications**

Enormously high data transmission

rate

**Internet application situation**

The Internet of Things (IoT) envisions thousands of constrained devices with sensing, actuating, processing, and communication capabilities able to observe the world with an unprecedented resolution. According to Cisco, more than 50 billion devices are expected to be connected to the internet by 2020 and 20% of which are from the industry sector [9]. These connected things will generate huge volume of data that need to be analyzed to gain insight behind this big IoT data. Moreover, in the industrial environments (industry 4.0) as well in in smart spaces (building, houses, etc.) and connected cars communications often require high reliability, low latency and scalability. Several technologies such as BLE, Zigbee, Wireless HART, 6TiSCH, LPWAN (Lora, Sigfox, etc.) have been proposed to fit these requirements. The forthcoming 5G networks are promising not only by increased data rates but also lowlatency data communication for latency-critical IoT applications. 5G will enable massive IoT devices connected via a myriad of networks and critical machine type communications. While the massive IoT is more concerned about scalability deep coverage and energy efficiency, the later requires ultra-low latency and extreme reliability (URLLC). Recently, the fog-to-thing continuum [10] is proposed to mitigate the heavy burden on the network due to the centralized processing and storing of the massive IoT data. Fog-enabled IoT architectures ensure closer processing in proximity to the things, which results in small, deterministic latency that enables real time applications and enforced security. The IoT is a modern and the state of the art archetype in the technological advancement which is evolving as a future Internet. As per the principal vision of the IoT, the further requirement is the ubiquity of the Internet, after connecting people anytime and everywhere, is to connect extinct entities. By providing objects with embedded communication capabilities and a common addressing scheme, a distributed and permeating network of impeccably connected diverse electric and electronic devices is designed, which is to be indigenously cohesive into the existing Internet connections and mobile networks. Formally, IoT can be defined as, " A worldwide network on electronically interconnected devices uniquely addressable, based on standard communication protocols and allows users to be A6 connected" [11]. Thus allowing for the development of new intelligent services available anytime, anywhere, by anyone and anything. Latest research work and

**IoT**

Provision connectivity to billions of devices with matching capability of power requirements for devices

**A6 connection Low end to end latency**

Provision of service to users with less than millisecond end-to-end delays in transmission and in switching of spectrum slots.

> technological systems are converging towards IoT and CRNs. Since the spectrum assignment policy involves expenditures for buying the RF spectrum, the assignment of spectrum for a huge number of devices and objects required for IoT connectivity will result in redundant cost effects. CRNs due to its typical spectrum utilization characteristic emerge in realization of IoT. The idea of a reserved spectrum slot as shared-to-reserve (SR) and reserved-to-share (RS) schemes in CR-HetNets proposed in [12] can enhance the system throughput and would offer a high bandwidth transmission for IoT-Us in CRN. A CRN properties enabling it for IoT applications is shown in **Figure 1**.

> Usually, the SU operates in half-duplex mode (HD), i.e., it can either transmit or sense at any instant of time [13]. Due to this HD operation of SU, there is a possibility that harmful interference to PU is caused on unexpected arrival of PU and its activity during the transmission of SU. Hence the spectrum sensing should be a continuous process and SU must vacate the licensed channel on arrival of its PU and switch to another suitable channel as per its application, i.e., a befitting spectrum decision framework is essential.

## **6. Need for Spectrum management in IoT based 5G/B5G networks**

External storage solutions offer nearly unlimited capacity, with dedicated signal processing to sort through data and find signals, interactions or events of interest. These long-duration, high-bandwidth solutions are ideal for today's crowded spectrum and advanced technologies such as cognitive radios. WRFS is characterized by PU activity modeling and accurate SS [14]. This means that the spectrum management holds a great significant in CR technologies and A6 connection. The DSA allows the users (both PUs and SUs) to optimally use the spectrum slots while guaranteeing their QoS requirements. Preserving the required QoS of the users along with their mobility requires spectrum mobility for the SUs in the network, which we now know as 5G network. Because of its mobility, an SU may change its location (cell) in a cellular network during its transmission and, therefore, will enter a new region in which the targeted RF spectrum slot is already being used by the PU [15]. The perfect SS techniques provide prior information for which SUs will work [5]. Since the primary traffic in any cell and region is always time varying and cannot be accurately predicted, therefore, the SUs must have the real time information of RF spectrum slots occupancy status to switch over to the vacant slots for resuming their transmission in case PUs arrive. Similarly, the SS errors are also required to be mitigated. The PUs use their licensed spectrum for their transmissions as per their QoS requirement and the statistical analysis says that this usage remains for a very short period of time. The decision of accessing the vacant spectrum slots would enable the SU to have A6 connections making an IoT environment. Therefore, SU is renamed here as IoT-User (IoT-U).

The allocated frequency spectrum for wireless applications is under-utilized due to the emblematic customs of wireless applications [16]. The conventional approach to spectrum management is very inflexible in the sense that each wireless service provider is assigned an exclusive license to operate in a certain frequency spectrum band. It has become very difficult to find vacant spectrum bands (to either deploy new services or to enhance existing ones) [17]. Therefore, for efficient utilization of the spectrum creating opportunities, Cognitive Radio (CR) technology allows its users called Secondary Users (SUs) to occupy the available spectrum slot for their communication (model is shown in **Figure 2**) and vacate it on arrival of the licensed user (called Primary User (PU)) [18]. The process of spectrum utilization using CR systems requires a Dynamic Spectrum Management Framework (DSMF) [19] which consists of four main components (naming Spectrum Sensing (SS), Spectrum Decision Framework, spectrum sharing and spectrum mobility). Spectrum sharing refers to coordinated access to the selected channel by the SUs. Spectrum mobility enables SU to switch over to another channel when a PU is arrived. SS involves identification of spectrum holes and the ability to quickly detect the onset of PU communications in the channels occupied by the SUs. Spectrum decision enables SUs in CR Network (CRN) to select the best available spectrum slots to satisfy SUs' Quality of Service (QoS) requirements without causing harmful interference to PUs [20]. On appearance of PU in the spectrum slot in which SU was carrying out its transmission, the SU looks for (which requires an efficient SS) and selects (which requires a suitable Spectrum Decision Framework) another QoS complying spectrum slot which is available/vacant. This implies that there are two steps (SS and spectrum decision framework) of efficient utilization of spectrum in CR-based IoT in 5G/B5G.

**6.1. Spectrum sensing**

CR systems offer the capability of IoT-Us to improve the spectrum utilization under the existing fixed spectrum assignment policy. IoT-Us cannot only sense the spectrum environment around and access vacant spectrum slots in the opportunity way, but also require to sense the presence of PU's signal continuously to keep the SS data updated. Hence, SS is the fundamental requirement in CR and the foundation for Spectrum Decision. The SS techniques are categorized into non cooperative (Energy Detection (ED), Matched Filter Detection (MFD) and cyclostationary detection), interference detection and cooperative (centralized SS (CSS) and non-centralized detection) [25, 26]. ED is the widely used scheme for SS due to its simplicity, easy implementation and it corresponds to the general purpose of SS for heterogeneous wireless communication systems [27]. The improved ED and CRs with multiple antennas can increase the SS performance [28]. MFD requires the exact synchronization and prior knowledge of PU signal, moreover implementation complexity of the sensing unit is large as the SU need receivers for all types of signal [29]. ED and MFD perform non coherent (by calculating the energy of the received signal samples) and coherent (comparing with the known PU signal) detection respectively [30]. Cyclostationary detection suffers from high complexity as all the cycle frequencies are required to be calculated [31]. CSS and non-centralized detection have exhibited SS errors due to time lag involved between sensing and its results [32]. Although the vacant spectrum slots are identified in SS but unless these are not simultaneously occupied through a well-defined decision process, the concept of CR cannot be realized. Therefore, it is imperative to mitigate the SS errors (false alarm and miss detection) before taking the decision to occupy the sensed vacant spectrum slot.

**Figure 2.** Working of a CRN model in which SU switched to another network on arrival of PU in primary network 1.

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81

A brief analysis of existing SS techniques [33] is given in **Table 4**.

There are two scenarios as an overlay and underlay for spectrum sharing [21, 22]. In spectrum overlay scenario, a SU accesses a RF spectrum slot only when it is not being used by the PU [23]. This scenario is also known as opportunistic spectrum access. In other scenario the SU coexists with the PU and transmits with power constraints to guarantee the quality of service (QoS) of the PU. This scenario is known as underlay spectrum sharing [24]. The overlay mode operation is focused in this chapter.

Spectrum Decision Framework to Support Cognitive Radio Based IoT in 5G http://dx.doi.org/10.5772/intechopen.80991 81

**Figure 2.** Working of a CRN model in which SU switched to another network on arrival of PU in primary network 1.

#### **6.1. Spectrum sensing**

and A6 connection. The DSA allows the users (both PUs and SUs) to optimally use the spectrum slots while guaranteeing their QoS requirements. Preserving the required QoS of the users along with their mobility requires spectrum mobility for the SUs in the network, which we now know as 5G network. Because of its mobility, an SU may change its location (cell) in a cellular network during its transmission and, therefore, will enter a new region in which the targeted RF spectrum slot is already being used by the PU [15]. The perfect SS techniques provide prior information for which SUs will work [5]. Since the primary traffic in any cell and region is always time varying and cannot be accurately predicted, therefore, the SUs must have the real time information of RF spectrum slots occupancy status to switch over to the vacant slots for resuming their transmission in case PUs arrive. Similarly, the SS errors are also required to be mitigated. The PUs use their licensed spectrum for their transmissions as per their QoS requirement and the statistical analysis says that this usage remains for a very short period of time. The decision of accessing the vacant spectrum slots would enable the SU to have A6 connections making an IoT environment. Therefore, SU is renamed here

The allocated frequency spectrum for wireless applications is under-utilized due to the emblematic customs of wireless applications [16]. The conventional approach to spectrum management is very inflexible in the sense that each wireless service provider is assigned an exclusive license to operate in a certain frequency spectrum band. It has become very difficult to find vacant spectrum bands (to either deploy new services or to enhance existing ones) [17]. Therefore, for efficient utilization of the spectrum creating opportunities, Cognitive Radio (CR) technology allows its users called Secondary Users (SUs) to occupy the available spectrum slot for their communication (model is shown in **Figure 2**) and vacate it on arrival of the licensed user (called Primary User (PU)) [18]. The process of spectrum utilization using CR systems requires a Dynamic Spectrum Management Framework (DSMF) [19] which consists of four main components (naming Spectrum Sensing (SS), Spectrum Decision Framework, spectrum sharing and spectrum mobility). Spectrum sharing refers to coordinated access to the selected channel by the SUs. Spectrum mobility enables SU to switch over to another channel when a PU is arrived. SS involves identification of spectrum holes and the ability to quickly detect the onset of PU communications in the channels occupied by the SUs. Spectrum decision enables SUs in CR Network (CRN) to select the best available spectrum slots to satisfy SUs' Quality of Service (QoS) requirements without causing harmful interference to PUs [20]. On appearance of PU in the spectrum slot in which SU was carrying out its transmission, the SU looks for (which requires an efficient SS) and selects (which requires a suitable Spectrum Decision Framework) another QoS complying spectrum slot which is available/vacant. This implies that there are two steps (SS and spectrum decision framework) of efficient utilization

There are two scenarios as an overlay and underlay for spectrum sharing [21, 22]. In spectrum overlay scenario, a SU accesses a RF spectrum slot only when it is not being used by the PU [23]. This scenario is also known as opportunistic spectrum access. In other scenario the SU coexists with the PU and transmits with power constraints to guarantee the quality of service (QoS) of the PU. This scenario is known as underlay spectrum sharing [24]. The overlay mode

as IoT-User (IoT-U).

80 Cognitive Radio in 4G/5G Wireless Communication Systems

of spectrum in CR-based IoT in 5G/B5G.

operation is focused in this chapter.

CR systems offer the capability of IoT-Us to improve the spectrum utilization under the existing fixed spectrum assignment policy. IoT-Us cannot only sense the spectrum environment around and access vacant spectrum slots in the opportunity way, but also require to sense the presence of PU's signal continuously to keep the SS data updated. Hence, SS is the fundamental requirement in CR and the foundation for Spectrum Decision. The SS techniques are categorized into non cooperative (Energy Detection (ED), Matched Filter Detection (MFD) and cyclostationary detection), interference detection and cooperative (centralized SS (CSS) and non-centralized detection) [25, 26]. ED is the widely used scheme for SS due to its simplicity, easy implementation and it corresponds to the general purpose of SS for heterogeneous wireless communication systems [27]. The improved ED and CRs with multiple antennas can increase the SS performance [28]. MFD requires the exact synchronization and prior knowledge of PU signal, moreover implementation complexity of the sensing unit is large as the SU need receivers for all types of signal [29]. ED and MFD perform non coherent (by calculating the energy of the received signal samples) and coherent (comparing with the known PU signal) detection respectively [30]. Cyclostationary detection suffers from high complexity as all the cycle frequencies are required to be calculated [31]. CSS and non-centralized detection have exhibited SS errors due to time lag involved between sensing and its results [32]. Although the vacant spectrum slots are identified in SS but unless these are not simultaneously occupied through a well-defined decision process, the concept of CR cannot be realized. Therefore, it is imperative to mitigate the SS errors (false alarm and miss detection) before taking the decision to occupy the sensed vacant spectrum slot. A brief analysis of existing SS techniques [33] is given in **Table 4**.

#### **6.2. PUs and IoT-Us activity model**

In CRNs, there are two types of users to use the WRFS, one is PU and the other is SU, which we have renamed as IoT-U in this chapter. Since FCC has approved the access of unlicensed users (IoT-Us) to the already sold RF spectrum provided the unlicensed users do not cause harmful interference to PUs. The performance of CRNs is largely dependent on PU arrival and departure from the spectrum slots, the license of which it holds [34]. Hence, it is very important to model the PU activity for CRNs to enable IoT-Us to decide for occupation idle spectrum slots. PUs in the wide range (kHz to GHz as UWB and 5G networks operate in 3.6–39 GHz) of WRFS operate in any spectrum depending upon the specific wireless applications. **Table 5** shows the operating radio frequency bands for various wireless technologies.

**SS Techniques Method Used Main Feature**

SUs to improve the detection reliability

by comparing the historical information of spectrum usage pattern with the received by a base station from each SU in the

signal directly to achieve efficient wide band sensing with much lower sampling rate than the Nyquist Criterion

vacant spectrum slots while sensing the

Cooperative SS Combining the sensing results of multiple

Spectrum-database SS Enables to find all available spectrum slots

network

Compressive SS Each SU detects and extracts the wide band

Full duplex SS Each SU in the network can access the

**Table 4.** List of SS techniques available in literature [33].

spectrum continuously

Based on PU's transmitter ED does not require the prior

information of the PU

PU's signal

Spectrum Decision Framework to Support Cognitive Radio Based IoT in 5G

the noise

chapter

MFD is related to prior knowledge of

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83

Cyclostationary detection relies on distinguish between the PU's signal and

The fusion mechanisms including reliability based cooperative decision fusion. One is described in section of this

Integrates quickest detection and belief

Guaranteeing the high sensing accuracy in vehicular networks or industrial

Exploiting spectrum table for SS A framework for determining the topology of vehicular network An iteratively developed history

A mobile crowd sensing-driven geolocation spectrum database for D2D

Based on real time PU's signal

Analyze the sparsity of the wideband

Spectrum occupancy status measurement

Joint mode/rate adaptation policy for

Using directional antenna Quashing interferences

propagation framework

wireless networks

processing database

Wideband SS scheme

Reducing SS errors

Listen and talk protocol

Optimal detection thresholds Canceling the self-interference of

communication

spectrum

WiFi/LTE-U At low SNR values

transceiver

ED, MFD and

based SS

Cyclostationary detection

As the RF spectrum band is wide range for various wireless applications, therefore, one PU activity cannot reflect the activity pattern of PU of all wireless applications as these varies from application to application. As the FCC has approved to use secondary users on licensed RF spectrum only with the condition that PU transmission will not be interfered. This implies that the licensed spectrum will only be occupied when PU is not using it, the underlay occupancy. Moreover, it is very important to ensure that PU is not harmfully interfered. That is why, the CRNs' performance is dependent on PU activity. Stochastic geometry provides a natural way of defining and computing macroscopic properties of mobile users' networks, by averaging over all potential geometrical patterns for the nodes, in the same way as queuing theory offers mobbing and the reliability, i.e., low end-to-end latency in wireless communication, average out the overall possible arrival patterns of the PUs within the networks on assigned RF spectrum. Thus PU activity modeling in wireless communication networks in terms of stochastic geometry is particularly relevant for spectrum decision framework. The PU activity, as a simplest case, in CRN can be represented as a print of a stationary random model in a probabilistic way. In particular the locations of the CRNs nodes are seen as the realizations of some point processes. When the underlying random model is ergodic, the probabilistic analysis also provides a way of estimating spatial averages which often capture the key dependencies of the CRN performance characteristics (connectivity, stability, capacity, etc.) as functions of a relatively small number of parameters [35]. Hence, the PU activity should be modeled with some stochastic arrival and departures probability expression. Poisson distribution process (PDP) provides a near to realistic probability of arrival and departure of the (primary) user in the network. PDP offers spatio-temporal representation of PU activity model. Moreover, Poisson distribution process is simple and adapts well in wireless communication scenario. The PU's arrival and departure follow the poison distribution process:

$$P(\text{k events in interval}) = \frac{\lambda\_p^k \mathbf{e}^{-\lambda\_p}}{\mathbf{k}!} \tag{1}$$

where *k* is occurrence of PU arriving and takes values *0,1,2,3, ....., N* and *λ<sup>p</sup>* is the arrival rate of PU in the spectrum slot in the CRN. The existing spectrum decision techniques model PU activities without taking into account for SU (IoT-U) behavior and characterization. This is desirable in this CRN growth era as well as in CRN based IoT-U to have its model defined. This will help in ensuring no interference and will provide basics for mechanism of switching

**Table 4.** List of SS techniques available in literature [33].

**6.2. PUs and IoT-Us activity model**

82 Cognitive Radio in 4G/5G Wireless Communication Systems

In CRNs, there are two types of users to use the WRFS, one is PU and the other is SU, which we have renamed as IoT-U in this chapter. Since FCC has approved the access of unlicensed users (IoT-Us) to the already sold RF spectrum provided the unlicensed users do not cause harmful interference to PUs. The performance of CRNs is largely dependent on PU arrival and departure from the spectrum slots, the license of which it holds [34]. Hence, it is very important to model the PU activity for CRNs to enable IoT-Us to decide for occupation idle spectrum slots. PUs in the wide range (kHz to GHz as UWB and 5G networks operate in 3.6–39 GHz) of WRFS operate in any spectrum depending upon the specific wireless applications. **Table 5** shows the operating radio frequency bands for various wireless technologies. As the RF spectrum band is wide range for various wireless applications, therefore, one PU activity cannot reflect the activity pattern of PU of all wireless applications as these varies from application to application. As the FCC has approved to use secondary users on licensed RF spectrum only with the condition that PU transmission will not be interfered. This implies that the licensed spectrum will only be occupied when PU is not using it, the underlay occupancy. Moreover, it is very important to ensure that PU is not harmfully interfered. That is why, the CRNs' performance is dependent on PU activity. Stochastic geometry provides a natural way of defining and computing macroscopic properties of mobile users' networks, by averaging over all potential geometrical patterns for the nodes, in the same way as queuing theory offers mobbing and the reliability, i.e., low end-to-end latency in wireless communication, average out the overall possible arrival patterns of the PUs within the networks on assigned RF spectrum. Thus PU activity modeling in wireless communication networks in terms of stochastic geometry is particularly relevant for spectrum decision framework. The PU activity, as a simplest case, in CRN can be represented as a print of a stationary random model in a probabilistic way. In particular the locations of the CRNs nodes are seen as the realizations of some point processes. When the underlying random model is ergodic, the probabilistic analysis also provides a way of estimating spatial averages which often capture the key dependencies of the CRN performance characteristics (connectivity, stability, capacity, etc.) as functions of a relatively small number of parameters [35]. Hence, the PU activity should be modeled with some stochastic arrival and departures probability expression. Poisson distribution process (PDP) provides a near to realistic probability of arrival and departure of the (primary) user in the network. PDP offers spatio-temporal representation of PU activity model. Moreover, Poisson distribution process is simple and adapts well in wireless communication scenario.

The PU's arrival and departure follow the poison distribution process:

where *k* is occurrence of PU arriving and takes values *0,1,2,3, ....., N* and *λ<sup>p</sup>*

of PU in the spectrum slot in the CRN. The existing spectrum decision techniques model PU activities without taking into account for SU (IoT-U) behavior and characterization. This is desirable in this CRN growth era as well as in CRN based IoT-U to have its model defined. This will help in ensuring no interference and will provide basics for mechanism of switching

<sup>k</sup> e<sup>−</sup>*λ<sup>p</sup>* \_\_\_\_\_

k! (1)

is the arrival rate

*<sup>P</sup>*(*<sup>k</sup> events in interval*) <sup>=</sup> *<sup>λ</sup>*<sup>p</sup>

**Table 5.** Frequency spectrum ranges for various wireless applications.

to other available slot, if PU arrives. The study of opportunistic spectrum access in CRNs with SU's transmission performance reveals that the interference caused to PU can be avoided by evaluating the SU's transmission blocking [34]. When PUs appear in the multiple spectrum slots in a WRFS band denoted by '*S*', IoT-Us need to vacate the spectrum slot and switch to another suitable spectrum slot (to complete their transmission) without interfering the PUs. This transmission process for PU and IoT-U is shown in **Figure 3**. This causes IoT-Us a temporary break in transmission, which is mitigated by simultaneous access in multiple noncontiguous spectrum bands by IoT-Us for their transmission. Even if a PU appears in one of the channels, the rest of the channels will continue to allow SUs to transmit while maintaining their QoS requirements.

In the transmission process of SUs when accessing the channel in a heterogeneous manner, the transmission level measure for PU is given by the PTB as,

the transmission level measure for PU is given by the PIB as,\\

$$P\_{\text{TB}} = P(i, j) = \frac{\lambda\_p (P\_{\text{'} \leftarrow i}) q (i + 1, j) + (j + 1) \,\mu\_\star P(i, j + 1) q (i, j + 1)}{i \,\mu\_p + j \,\mu\_\star, \lambda\_p + \lambda\_\star} \tag{2}$$

and

*ω‾*

**Figure 3.** Transmission process for PUs and SUs (IoT-Us) to mitigate interference.

*<sup>ϕ</sup>*(*i*, *<sup>j</sup>*) <sup>=</sup> {

**7. Spectrum decision framework**

= {(*i*, *j*)|0 ≤ *i* ≤ *N*; 0 ≤ *j* ≤ *N*} (3)

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0, otherwise (4)

1, (*i*, *j*) ϵ

CR Technology is characterize by PUs and SUs, a coordination among the IoT-Us to use the licensed bands when PUs are not using spectrum slots in the targeted spectrum slot. Although, in the recent 5 years, the researchers have carried out work in the area of Spectrum Decision, however, still this area is not yet fully explored. A survey of spectrum decision in CRNs based on RF spectrum characterization, spectrum selection and CR reconfiguration has been presented in [20]. Since then (2013) in one of spectrum decision research works, the authors have proposed a fuzzy inference-based decision strategy which is based on three key parameters, spectrum slot idle time, spectrum slot occupancy status and spectrum slot QoS [36] which provides an accurate and robust spectrum decision framework for SUs. The same and can be equally effective for IoT-Us in CR-based IOT in 5G/B5G networks as it encompasses all signal processing matrices such as Massive MIMO Antennas, varying bandwidths characteristics and wireless propagation channel models for all wireless applications (through spectrum slot QoS), required for wireless communications. Likewise, a spectrum decision scheme is

*ω‾*

where PUs and IoT-Us arrive and depart from each spectrum slot in *s* at the rate *λ<sup>p</sup>* and *λ<sup>s</sup>* respectively. Similarly, the *μp* and *μs* are the mean values of the respective transmission durations of PU and SU in the network. The number of spectrum slots in *s* by PU and IoT\_U at some specific time are represented by *i* and *j* respectively*,* such that *i + j* ≤ *N.* P (i,j) is the stationary probability of two dimensional Markov state which is *PTB.*

The state space *ϖ* for PUs and IoT-Us occupying spectrum slot in *S* is given as under;

Spectrum Decision Framework to Support Cognitive Radio Based IoT in 5G http://dx.doi.org/10.5772/intechopen.80991 85

**Figure 3.** Transmission process for PUs and SUs (IoT-Us) to mitigate interference.

$$\overline{\omega} = \left\{ (\mathbf{i}, \mathbf{j}) \,|\, 0 \le \mathbf{i} \le N; 0 \le j \le N \right\} \tag{3}$$

and

to other available slot, if PU arrives. The study of opportunistic spectrum access in CRNs with SU's transmission performance reveals that the interference caused to PU can be avoided by evaluating the SU's transmission blocking [34]. When PUs appear in the multiple spectrum slots in a WRFS band denoted by '*S*', IoT-Us need to vacate the spectrum slot and switch to another suitable spectrum slot (to complete their transmission) without interfering the PUs. This transmission process for PU and IoT-U is shown in **Figure 3**. This causes IoT-Us a temporary break in transmission, which is mitigated by simultaneous access in multiple noncontiguous spectrum bands by IoT-Us for their transmission. Even if a PU appears in one of the channels, the rest of the channels will continue to allow SUs to transmit while maintain-

5G Cellular 26.5–40 GHz and 30–50 GHz All ranges of bandwidths i.e., narrow, wide,

In the transmission process of SUs when accessing the channel in a heterogeneous manner,

where PUs and IoT-Us arrive and depart from each spectrum slot in *s* at the rate *λ<sup>p</sup>*

The state space *ϖ* for PUs and IoT-Us occupying spectrum slot in *S* is given as under;

tions of PU and SU in the network. The number of spectrum slots in *s* by PU and IoT\_U at some specific time are represented by *i* and *j* respectively*,* such that *i + j* ≤ *N.* P (i,j) is the

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ *<sup>i</sup>* μp <sup>+</sup> *<sup>j</sup>* μs+ *<sup>λ</sup><sup>p</sup>* <sup>+</sup> *<sup>λ</sup><sup>s</sup>*

200 KHz

20 MHz

ultra wide and super ultra wide bands

communication systems

are the mean values of the respective transmission dura-

(2)

and *λ<sup>s</sup>*

the transmission level measure for PU is given by the PTB as,

**Wireless applications Frequency spectrum bands Bandwidth** IEEE 802.11 g to n/WiFi 2.4 GHz 10 KHz IEEE 802.16/LAN/2 5 GHz 100 KHz IEEE 802.22 54–862 MHz 5–20 MHz

CDMA 800 and 1.9 GHz 125 MHz

UWB 3.1–10.6 GHz 500 MHz

**Table 5.** Frequency spectrum ranges for various wireless applications.

935–960 MHz (downlink)

2110–2170 MHz (downlink)

W-CDMA 850–2100 MHz 125 MHz, 250 MHz

GSM 890–915 MHz (uplink)

84 Cognitive Radio in 4G/5G Wireless Communication Systems

LTE 1710 –1770 MHz (uplink)

and *μs*

stationary probability of two dimensional Markov state which is *PTB.*

*PTB* <sup>=</sup> *<sup>P</sup>*(*i*, *<sup>j</sup>*) <sup>=</sup> *<sup>λ</sup>p*(*Pi*−*i*,*<sup>j</sup>*)*ϕ*(*<sup>i</sup>* <sup>+</sup> 1.*j*) <sup>+</sup> (*<sup>j</sup>* <sup>+</sup> 1) *μs <sup>P</sup>*(*i*, *<sup>j</sup>* <sup>+</sup> 1)*ϕ*(*i*.*<sup>j</sup>* <sup>+</sup> 1)

ing their QoS requirements.

respectively. Similarly, the *μp*

$$\varphi(\mathbf{i}, \mathbf{j}) = \begin{cases} \ 1, (\mathbf{i}, \mathbf{j}) \in \varpi \\ 0, \text{otherwise} \end{cases} \tag{4}$$

## **7. Spectrum decision framework**

CR Technology is characterize by PUs and SUs, a coordination among the IoT-Us to use the licensed bands when PUs are not using spectrum slots in the targeted spectrum slot. Although, in the recent 5 years, the researchers have carried out work in the area of Spectrum Decision, however, still this area is not yet fully explored. A survey of spectrum decision in CRNs based on RF spectrum characterization, spectrum selection and CR reconfiguration has been presented in [20]. Since then (2013) in one of spectrum decision research works, the authors have proposed a fuzzy inference-based decision strategy which is based on three key parameters, spectrum slot idle time, spectrum slot occupancy status and spectrum slot QoS [36] which provides an accurate and robust spectrum decision framework for SUs. The same and can be equally effective for IoT-Us in CR-based IOT in 5G/B5G networks as it encompasses all signal processing matrices such as Massive MIMO Antennas, varying bandwidths characteristics and wireless propagation channel models for all wireless applications (through spectrum slot QoS), required for wireless communications. Likewise, a spectrum decision scheme is

**Figure 4.** Proposed Spectrum decision framework for CRN based IoT in 5G/B5G networks.

proposed here with an analysis through Radio Operating characteristics (ROC) curves at various SNR values. This scheme is based on fusion of three separate decision of three key parameters, spectrum slot idle time, spectrum slot occupancy status and the spectrum slot performance. The spectrum decision framework first finds the idle time of the spectrum slot, spectrum slot occupancy status through SS using ED scheme and spectrum lot performance based on its ergodic capacity as shown in **Figure 4**.

RF spectrum slots in CRN are considered to have varying bandwidth. There are *N* PUs having rights to access same number of corresponding spectrum slots. *J* IoT-Us are attempting to access these slots for their transmission. Wireless services employ a combined FDMA/TDMA

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The simulation parameters are given in **Table 6**. In finding the idle spectrum slot through the PU activity time, there can occur two types of errors. One is, false alarm and the other is the miss detection. Later is due to sensing an idle spectrum slot as occupied, and the following is due to assuming an occupied slot as idle. The performance of the proposed spectrum decision framework is assessed here through ROC curves between probability of false alarm and miss detection. These two are the inter-related parameters in the proposed decision process. To

approach for air interface.

**Table 6.** Simulation parameters.

**7.2. Performance evaluation and numerical analysis**

No. of BTSs 5 No of IoT-Us in CRN *J* No of PUs 5

Wireless channel model 3 GPP Bandwidth 200 KHz

RF spectrum range 890–915 MHz (GSM Band)

**Figure 5.** System model for proposed Spectrum decision framework.

### **7.1. System model**

A CRN operating in a spectrum band '*S'* with frequency ranging from 54 MHz–50 GHz covering most of the wireless applications given in **Table 5**, is considered. The other specifications to be considered (like uplink and downlink frequencies, modulation techniques used in transmission and bandwidth) are as given in [37]. Channel bandwidth is the frequency range over which a IoT-U's transceiver transmits and receives its signals in CRN. An IoT-U can carry out its transmission on either narrow, wide and ultra wide band (UWB) ranges depending on the RF environment and wireless applications. The CRN has a centralized network operator, for instance a BTS which functions as "serve to provide". A region comprising of 5 BTSs, unlimited number of mobile devices, all buildings in the neighborhood are under the coverage of all the wireless services as shown in **Figure 5**. A wide range wireless based applications, i.e., GSM, bluetooth, UWB, NB, video conferencing, IP based communication, office automation systems, building security management systems, 5G and RFID, connected through IP based communication radios. 3GPP channel model has been used owing to its typical characteristics for wireless systems, i.e., it has properties that impact on system performance by reflecting the important properties of propagation channels. Moreover, wireless networks are optimized in the region of system model. Let there be '*J*' SUs (using ED for SS) in the CRN each having its own Software Defined Radio (SDR) to exploit the multiple spectrum bands over wide spectrum ranges by adjusting the operating frequency through software operations. The BTSs exercises control over all *J* IoT-Us within its transmission range as shown in **Figure 2**. The Spectrum Decision Framework to Support Cognitive Radio Based IoT in 5G http://dx.doi.org/10.5772/intechopen.80991 87

**Figure 5.** System model for proposed Spectrum decision framework.

proposed here with an analysis through Radio Operating characteristics (ROC) curves at various SNR values. This scheme is based on fusion of three separate decision of three key parameters, spectrum slot idle time, spectrum slot occupancy status and the spectrum slot performance. The spectrum decision framework first finds the idle time of the spectrum slot, spectrum slot occupancy status through SS using ED scheme and spectrum lot performance

**Figure 4.** Proposed Spectrum decision framework for CRN based IoT in 5G/B5G networks.

A CRN operating in a spectrum band '*S'* with frequency ranging from 54 MHz–50 GHz covering most of the wireless applications given in **Table 5**, is considered. The other specifications to be considered (like uplink and downlink frequencies, modulation techniques used in transmission and bandwidth) are as given in [37]. Channel bandwidth is the frequency range over which a IoT-U's transceiver transmits and receives its signals in CRN. An IoT-U can carry out its transmission on either narrow, wide and ultra wide band (UWB) ranges depending on the RF environment and wireless applications. The CRN has a centralized network operator, for instance a BTS which functions as "serve to provide". A region comprising of 5 BTSs, unlimited number of mobile devices, all buildings in the neighborhood are under the coverage of all the wireless services as shown in **Figure 5**. A wide range wireless based applications, i.e., GSM, bluetooth, UWB, NB, video conferencing, IP based communication, office automation systems, building security management systems, 5G and RFID, connected through IP based communication radios. 3GPP channel model has been used owing to its typical characteristics for wireless systems, i.e., it has properties that impact on system performance by reflecting the important properties of propagation channels. Moreover, wireless networks are optimized in the region of system model. Let there be '*J*' SUs (using ED for SS) in the CRN each having its own Software Defined Radio (SDR) to exploit the multiple spectrum bands over wide spectrum ranges by adjusting the operating frequency through software operations. The BTSs exercises control over all *J* IoT-Us within its transmission range as shown in **Figure 2**. The

based on its ergodic capacity as shown in **Figure 4**.

86 Cognitive Radio in 4G/5G Wireless Communication Systems

**7.1. System model**

RF spectrum slots in CRN are considered to have varying bandwidth. There are *N* PUs having rights to access same number of corresponding spectrum slots. *J* IoT-Us are attempting to access these slots for their transmission. Wireless services employ a combined FDMA/TDMA approach for air interface.

## **7.2. Performance evaluation and numerical analysis**

The simulation parameters are given in **Table 6**. In finding the idle spectrum slot through the PU activity time, there can occur two types of errors. One is, false alarm and the other is the miss detection. Later is due to sensing an idle spectrum slot as occupied, and the following is due to assuming an occupied slot as idle. The performance of the proposed spectrum decision framework is assessed here through ROC curves between probability of false alarm and miss detection. These two are the inter-related parameters in the proposed decision process. To

**Table 6.** Simulation parameters.

have accurate data of PU(s) activity time and occupancy status, there should be low values of both the probabilities, which cannot be achieved as both are inter related to each other. Therefore, an optimal set of range must be obtained. Transmission at different values of SNR give different ROC curves for the PU's activity time and its occupancy in the spectrum slot. The lower the probability of miss detection (or higher the probability of detection) for a given probability of false alarm, the more reliable and accurate the detection would be, which is desirable for taking the decision by the SU(s) to occupy that particular spectrum slot in CRN.

Energy Detection (ED) in SS offers a fast and reliable detection method for SUs in CRN. The detection performance of ED depends on effects of multipath fading [38]. Accordingly, the proposed decision framework has been validated by ROC curves compared with those of SS through ED method at various SNR values. ROC curves for 5 IoT-Us are shown in **Figure 6**. For the communication overhead as an outcome of information exchange required by the statistical approach, has been significantly reduced by using existing common control channels (CCC) by IoT-Us. This complexity cost is fully justified given the significant performance improvement that the proposed framework offers in terms of latency, throughput, energy efficiency, delay, and the reliability for realization of IoT in terms of A6 connections. To ensure there is no harmful interference caused to PU by the SU, the SU's transmitter trans-

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CR is an important measure to spectrum scarcity problem. To optimally utilize the already allocated spectrum, spectrum decision holds significance in CR. Spectrum decision enables CR users to access the spectrum slots as per their wireless application over a wide RF spectrum range. In this chapter a spectrum decision framework has been proposed which weighs the spectrum band on its idle time, occupancy status and performance and ensures A6 connection thereby providing an enable technology for IoT to support 5G/B5G networks with

mits less energy in the side lobs of the transmission signal as shown in **Figure 7**.

Ahmad Naeem Akhtar\*, Fahim Arif and Adil Masood Siddique \*Address all correspondence to<EMAIL_ADDRESS>
National University of Science and Technology (NUST), Islamabad, Pakistan

[1] Atzori L, Iera A, Marobito G. The internet of things: A survey. Computer Networks.

[2] Roy A, Sengupta S, Wong K-K, Raychoudhury V, Govindan K, Singh S. 5G wireless with cognitive radio and massive IoT. IETE Technical Review. 2017;**34**(suppl 1):1-3. DOI:

[3] Liu X, He D, Jia M. 5G-based wideband cognitive radio system design with cooperative spectrum sensing. Physical Communication. 2017;**25**(2):539-545, . ISSN 1874-4907. DOI: 10.1016/j.phycom.2017.09.010. (http://www.sciencedirect.com/science/article/pii/

**8. Conclusion**

higher data rates.

**Author details**

**References**

2010:2787-2805

S1874490717302483)

10.1080/02564602.2017.1414387

**Figure 6.** ROC curves for various values of SNR compared with ED SS results and on occupying the spectrum slots by 5 IoT-Us under proposed frequency Spectrum decision mechanism (FSDM).