# UAV-assisted C-RAN for On-demand Cellular Coverage: Opportunities and
Challenges

Byomakesh Mahapatra, Deepika Gupta, and Pankaj Kumar Sharma Byomakesh
Mahapatra and Pankaj Kumar Sharma are with the National Institute of
Technology Rourkela, India.Deepika Gupta is with the Dr. Shyama Prasad
Mukherjee International Institute of Information Technology Naya Raipur,
India.

###### Abstract

The deployment of beyond fifth-generation (5G) infrastructure over disaster-
affected regions, temporary hotspot situations (e.g., massive gatherings,
etc.), complex terrains (e.g., sea, hills, marshes, etc.) poses numerous
challenges for cellular service providers. Recently, unmanned aerial vehicles
(UAVs) have emerged as potential candidates to overcome the aforementioned
technical issues based on their multi-role capabilities to serve as aerial
base stations, mobile relays, and flying wireless access points. As such, the
UAVs can act as portable platforms that can be deployed immediately on demand
without requiring massive ground infrastructure to support wireless services.
This article introduces the integration of UAVs to cloud radio access networks
(C-RAN) for beyond 5G applications. The article mainly focuses on the
underlying opportunities and challenges to realize the UAV-assisted C-RAN (UC-
RAN) architecture in view of three generic application scenarios, i.e.,
disaster management, hotspots, and complex terrains. A preliminary performance
analysis via simulation is further provided for the proposed UC-RAN under
hotspot application scenario based on the relevant metrics.

###### Index Terms:

Cloud radio access network (C-RAN), beyond $5$G communications, UAV-assisted
C-RAN (UC-RAN), mobile networks.

##  I Introduction

Cloud radio access network (C-RAN) architecture is of tremendous importance in
fifth-generation (5G) and beyond cellular communications, especially due to
its low energy consumption and implementation cost [1]. The C-RAN is a
technology that reduces computational complexity and supports multi-radio
access technology (M-RAT) which is of great relevance for next-generation
heterogeneous networks. However, at certain places such as complex terrains
(e.g., hills, marshes, lakes, sea, etc.), deep forests, disaster regions,
etc., the C-RAN encounters severe deployment challenges due to limited
accessibility, unavailability of power supply, non-line-of-sight (non-LoS)
problem, deep fading, etc. To overcome these problems the cellular operators
either need to deploy a larger number or a bigger size of the base stations,
which directly leads to be a costly affair for them. Further, in hotspot cells
where traffic at a base station (BS) exceeds a predetermined threshold value,
the user equipment (UEs) may not be serviced without compromising their
quality of service (QoS). Hence, there is a need of a flexible and scalable
RAN platform [2] which enables on-demand coverage to ensure reliable
communications in aforementioned situations. Unmanned aerial Vehicles (UAVs)
have recently emerged as potential candidates for on-demand services in next-
generation cellular networks due to their easy deployment as aerial BS and
wireless access points [3]. For instance, in [4], the UAV has been deployed as
wireless access points using Wi-Fi under disaster situations. The UAVs can be
deployed in the role of relays to extend coverage over complex terrains.
Further, the UAVs can be deployed as small BSs in a hotspot cell to offload
traffic from an overloaded ground BS (GBS). However, the deployment strategy
to be employed for UAVs depends upon the specific application scenario. The
main challenge associated with deploying UAVs is the limited battery power
onboard. In recent years, i.e., from 2016 to 2022, a market growth of nearly
300% has been observed in the overall use of UAVs related to military,
commercial, and consumer applications [5], [6]. The much-anticipated increase
in the use of UAVs in future networks would require more sophisticated
connection and traffic management strategies. Additionally, for delay-
sensitive applications, a high data rate supporting robust communication links
would be needed.

Furthermore, a GBS in existing cellular networks, due to is large size, incurs
very high total costs in terms of capital expenditure (CAPEX) and operational
expenditure (OPEX). In practice, a GBS is built upon traditional C-RAN
architecture which is unscalable in nature. As such, a GBS consumes large
static power (i.e., the minimum power required to operate the base station
under zero traffic load condition) as compared to a small aerial BS.
Therefore, this paper explores a flexible and scalable UAV-assisted C-RAN (UC-
RAN) architecture for next-generation cellular networks. Note that in C-RAN, a
GBS consists of two major units, namely remote radio head (RRH) and baseband
Unit (BBU). Here, in UC-RAN, the UAVs acting as flying RRHs (F-RRHs) are
integrated with traditional C-RAN for on-demand coverage. The specific role of
F-RRH (i.e., aerial BS, relay, wireless access point, etc.) depends upon the
particular application scenario. In general, an F-RRH is equipped with a
transceiver unit connected to a static RRH (S-RRH) through the wireless
fronthaul. This fronthaul works based on the 3rd Generation Partnership
Project (3GPP) 7.2 C-RAN functional split [7]. The advantage of this split is
that it minimizes the required fronthaul bandwidth by performing some RF-
related baseband function at the F-RRH itself. Further, S-RRHs are connected
BBU pool via fixed fronthaul optical links and to the UEs through RF channels.
As such, the fixed S-RRH and BBU pool placement in C-RAN is not suitable for
quick service recovery under on-demand coverage applications. Furthermore, for
applications like disaster management, remote area surveillance, or smart city
traffic monitoring there is a need for a delay-aware data delivery system to
take quick action and responses. These delay-sensitive data deliveries can be
obtained either by providing a larger bandwidth to the system or by bringing
the computing nodes nearer to the sender node (i.e., UE). The F-RRH-enabled
Mobile Edge Computing (MEC) brings computing resources to the F-RRH that
process and execute the incoming task over a local edge processor. An MEC-
enabled F-RRH is shown in Fig. 1. It consists of three units, i.e., a
computing unit, a storage unit, and a transceiver unit. The computing unit is
equipped with a number of virtual machines (VMs) to process the tasks assigned
by the associated UEs (or other UAVs). Each VM is separated by the others
through a guest operating system and can handle different tasks parallelly
over a single platform. The storage comprises of low-end memory devices to
store any intermediate data generated by the local computations. The computing
and storage units together help in reducing the computational load on the core
network and associated delay through distributed edge processing. Here, the
UAV transceiver coordinates the connection between the S-RRH and UAV edge
node. Backed by features such as cost-effectiveness, low computational
complexity, and less energy consumption, the UC-RAN architecture incorporating
the F-RRHs is of great importance for on-demand coverage.

Figure 1: Mobile edge computing-enabled UAV RRH.

Motivated by the above, this article introduces the integration of UAVs to
C-RAN for relevant applications in $5$G and beyond systems. Specifically, a
detailed discussion is provided on the opportunities and underlying
implementation challenges related to UC-RAN architecture in the context of the
following generic application scenarios: disaster management, hotspots, and
complex terrain. Further, a preliminary simulation-based performance analysis
of the proposed UC-RAN architecture in terms of service delay, UE blocking
probability, and total power consumption will be provided to draw useful
insights.

This article has been organized as follows: Section II provides the details of
UAV-assisted RAN architecture, application scenarios, and state-of-the-art.
Section III addresses the key implementation issues for UC-RAN. Section IV
discusses monitoring and control methods for considered UC-RAN applications.
Section V deals with the performance evaluation of the proposed UC-RAN
architecture. Finally, a conclusion is drawn in Section VI.

##  II UC-RAN Architecture: Overview and State-of-the-art

The conventional C-RAN architecture consists of S-RRHs connected to a BBU pool
via fixed point-to-point fronthaul links. The restricted fronthaul link and
time-consuming S-RRH deployment limit its cellular services in some on-demand
situations, e.g., natural disasters, hotspots, complex terrain, etc. So future
cellular networks need a UAV BS or F-RRH as part of it to extend coverage and
provide on-demand dynamic capacity of the ground base station infrastructure.
The UC-RAN architecture UAVs are mounted with a transceiver unit and it is
called F-RRH. The F-RRHs possess wireless connectivity with the S-RRHs through
a wireless frontahul link. Due to its deployment flexibility, an F-RRH can
take on various technical roles, e.g., a relay node between the S-RRH and UE,
a wireless access point to UEs backhauled by the S-RRH, and a mobile edge node
in on-demand situations. Further, the F-RRHs in UC-RAN can be of two types,
namely passive and active. In particular, a passive F-RRH has limited
functionality as it performs only simple RF signal processing such as
uplink/downlink conversion and data framing. Therefore, the passive F-RRHs are
primarily suited for deployment either as a relay node or a wireless access
point in UC-RAN. On the contrary, an active F-RRH is enabled by the mobile
edge cloud (MEC) computing platform to perform both the RF and baseband signal
processing onboard the edge node. Hence, the MEC-enabled F-RRH can act as an
autonomous aerial BS which can reduce the associated delay and dependency on
BBU pool. Moreover, by incorporating control and management functionalities at
the F-RRH edge node, it becomes a suitable candidate for many mission-critical
and disaster management application scenarios. Table I highlights some key
benefits and limitations of UC-RAN architecture keeping in view the
traditional C-RAN architecture. Here, we mainly included the parameters that
are closely related to the on-demand deployment of base stations for rapid
cellular connectivity over the target areas. Table I reveals that the UC-RAN
architecture is advantageous in terms of enhanced coverage, capacity,
deployment flexibility, etc. However, the penalty incurred due to the use of
F-RRHs is the increased complexity and reduced robustness of UC-RAN
architecture for on-demand requirements.

TABLE I: Key advantages and limitations of UC-RAN architecture Advantage(s) | Limitation(s)
---|---
Support on-demand RRH deployment | F-RRH is not robust
Support on-demand coverage enhancement | F-RRH operational time is limited
Support multi-altitude F-RRH placement | Increases BS complexity
Required less CAPEX for on-demand RRH placement | F-RRH durability is less

A UC-RAN architecture is shown in Fig. 2 for typical on-demand applications.
These applications include scenarios such as cellular coverage extension in
complex terrains, user management in hotspots, disaster management, and fire-
fighting operations. Here, the UC-RAN architecture uses either passive or
active F-RRHs based on the application scenario. For instance, in complex
terrains, the F-RRH acts as an intermediary relay node between the S-RRH and
UEs. Similarly, in a hotspot situation, there is a need of a temporary
wireless access point to serve UEs facing network congestion from overloaded
BS. This can be resolved by temporarily deploying some passive F-RRHs within
the coverage of an S-RRH. These temporary F-RRHs effectively shed the load
from the congested BS by serving the UEs. Furthermore, in disaster situations
when the ground S-RRH gets damaged, both the passive and active F-RRH
deployment is necessary for quick service recovery. Here, the active F-RRHs
act as central nodes that control various associated passive F-RRHs engaged in
disaster recovery and relief operations. A few works are available in the
literature that have considered UAV-assisted BS for on-demand situations. The
authors in [8] have proposed a tethered-UAV architecture. The tether link
between UAV and GBS helps to increase flight time due to the availability of
continuous power supply but limits the UAV coverage area. In [9], the authors
have considered UAV-assisted BS for dynamic coverage in a heterogeneous
cellular network. The article discusses various UAV deployment strategies by
considering interference and frequency reuse.

Figure 2: UC-RAN architecture for typical application scenarios.

The state-of-the-art for UC-RAN for B$5$G is currently in nascent stage. The
authors in [10] have proposed a UC-RAN architecture where an RRH unit is
mounted onto a UAV, so that it can work as a BS. The authors have developed
optimal strategies for user association, power allocation, UAV locations for
data rate maximization in a cellular cell. Further, the authors in [11] have
presented a feasibility study for integrating the UAVs to C-RAN architecture.
Here, the UAV is considered as a substitute BS to serve under the breakdown of
the main BS. To enhance the coverage, the rate splitting technique was
considered between UAV and BS. In [12], the authors have compared the service
latency for traffic assignment in an MEC-enabled UAV network and a macro BS-
based cellular network. It was shown that UAV-based network outperforms the
macro BS-based cellular network in terms of service latency. Additionally, it
was depicted that integrating UAVs to existing cellular infrastructure makes
the BS scalable and energy-efficient. A hybrid UC-RAN architecture with MEC is
proposed in [13]. Here, the authors aim to minimize the transmit power based
on optimizing the user association, computational resources, and UAV location.
The authors in [14] have proposed a UAV-assisted multi-tier MEC architecture
for internet-of-things (IoT) applications. Here, the multiple UAVs have been
used to serve ground IoT devices. The authors have applied a differential
algorithm for optimal UAV placement and the deep reinforcement learning method
for task assignment. In [15], the authors have applied compressive sensing and
convex optimization techniques for user association, computational resource
allocation, and location planning in a UAV-assisted MEC network. In [16], an
MEC-enabled UAV network was proposed for disaster management and rescue
operations. It consists of two layers of UAVs with different processing and
controlling capabilities. Further, the authors have optimized the utilization
of computational and communication resources in multi-tier UAV networks.

##  III  Key Implementation Issues for UC-RAN

This section addresses the various key implementation issues for UC-RAN.

###  III-A Deployment Issues

The deployment of UAVs in UC-RAN poses several challenges which are described
below:

Dependency on target application scenarios: The deployment of UAVs in UC-RAN
architecture depends on the target application scenarios. For instance, the
deployment in disaster situations targets the search and rescue operations,
supply of essential commodities, medical aid, etc. Here, an event-based UAV
communications infrastructure needs to be employed. This can be technically
achieved by the use of multi-hop UAV communications where an intermediate
centralized F-RRH provides connectivity to other F-RRHs engaged in disaster
management. It is worth mentioning that the centralized F-RRH is of active
type with wireless backhaul connectivity to the S-RRH of a nearby undamaged
BBU pool. Further, for certain applications such as agricultural land survey,
wildlife monitoring, and reconnaissance missions, a schedule-based UAV
communications infrastructure is essential. Here, the F-RRHs require
occasional connectivity which can be supported by the available cellular
network infrastructure. However, for deployment in complex terrains, smart
cities, industrial and healthcare monitoring, etc., a permanent UAV
communications infrastructure is important which poses new technical
challenges.

Dependency on weather conditions: The deployment of UAVs in UC-RAN further
depends on the weather conditions at a particular geographical region. The
weather conditions result in various hazards to UC-RAN, viz., moderate,
adverse and severe [17]. The moderate hazards involve poor visibility due to
heavy clouds, fog, glare from sun, etc. for the physical deployment of F-RRHs.
Furthermore, the adverse hazards occur due to heavy wind, turbulence, heavy
rain, and snow fall at the target geographical region. This type of hazards
causes problems such as the loss of communications due to unstable aerodynamic
control of UAVs (e.g., drifting, wobbling, crashing, etc.). More importantly,
the severe hazards are most disastrous among all which occur due to the
lighting, hail, tornadoes, etc. The severe hazards are most disastrous as they
can cause damage not only to the F-RRHs but also to their controlling unit and
ground BS.

Dependency on altitude and LoS: The altitude of F-RRHs is a key parameter for
their safe and efficient operation. The altitude of UAVs in general is limited
by the government regulations (e.g., for safer operation of commercial flight,
etc.) as well as the terrain characteristics of target geographical region.
Further, the LoS is the maximum range upto which the F-RRHs can see the S-RRHs
at ground cellular infrastructure. The path loss in communication between
F-RRHs and S-RRHs strongly depends on the LoS. With the increase in altitude
of F-RRHs up to certain optimal point, the LoS improves and path loss
decreases. However, beyond the optimal point, the LoS diminishes and path loss
increases drastically. As mentioned previously, the altitude of F-RRHs is
dependent on the terrain characteristics of a geographical region, the optimal
altitude of F-RRHs is a critical factor for reliable communications and safer
operation [18].

###  III-B Design Issues

The main design issues in UC-RAN are described below:

Payload and Weight of UAVs: Payload is referred to as the maximum weight of
equipment a UAV can carry during flight. The equipment includes the electronic
circuitry, batteries, antennas, etc. The size of F-RRHs is directly related to
their payload capacity. For instance, increasing the size of F-RRHs limits
their endurance due to increased weight and shortened battery lifetime. Hence,
the F-RRHs with discharged batteries need to be replaced frequently which
require efficient admission control and handover strategies.

Power consumption: The flying time of F-RRHs is limited by their battery size
and the minimization of power consumption in UC-RAN is a key issue. The
available power onboard the F-RRHs is utilized to power propellers for
mechanical flight and to power electronic circuits for communications. The
major power consumption in F-RRHs takes place in the mechanical flight which
can only be reduced by a better aerodynamic design. Whereas, the reduction of
electronic power consumption can be achieved by incorporating the
architectural and functional changes at F-RRHs and the BBU pool in UC-RAN.
Power consumption at the BBU pool can be reduced by using centralization and
virtualization at F-RRHs edge nodes. The electronic power at F-RRHs can also
be reduced by an energy-efficient communication design.

Service latency: It is apparent that a UC-RAN involve multi-hop communication
between S-RRHs and F-RRHs for various critical applications. In general, most
of these applications are delay-sensitive which requires minimal end-to-end
(E2E) latency for reliable communications. The latency minimization in UC-RAN
is also a critical design issue. A possible solution for E2E latency reduction
in UC-RAN is to incorporate edge computing at F-RRH. The local computations at
edge nodes help reducing the overall E2E latency.

UE blocking probability : UE Blocking probability in UC-RAN is the chance that
a UE fails to connect with an S-RRH or F-RRH within a stipulated time period.
In a cellular hotspot, the limited bandwidth availability at the S-RRH
restricts some of the UEs to get serviced. This causes the increase in UEs’
blocking probability at the S-RRH. In UC-RAN, this problem can be effectively
tackled by the F-RRHs which act as temporary access points to offload UEs from
an overloaded S-RRH in a hotspot cell.

Hovering time: Hovering time of F-RRHs is an important metric for multi-user
communication scenario. Here, an F-RRH acts as a wireless access point for
serving multiple groups of users in a cellular cell. The F-RRH sequentially
serves multiple group of UEs by hovering for optimal time at certain strategic
locations. The hovering time optimization of F-RRHs targeting the sum-rate
maximization of associated UEs is an important design problem for UC-RAN.

##  IV Monitoring and Control Strategies for F-RRHs in UC-RAN

The monitoring and control strategies for F-RRHs are presented for the three
generic application scenarios as described previously.

Figure 3: Implementation of UC-RAN for typical application scenarios.

###  IV-A Disaster Management

In disaster situations, e.g., earthquake, tsunami, cyclones, etc., there is an
immediate need to establish communications to effectively perform the relief
operations. For this purpose, as shown in Fig. 3, a group of F-RRHs can be
employed to provide wireless connectivity in the disaster hit area with a
nearby working BBU pool. These F-RRHs take on various roles with dedicated
wireless links as shown in the figure. For instance, F-RRH2 equipped with a
camera performs the geographical survey of disaster hit region. The F-RRH3 and
F-RRH4 are considered to be the intermediate relay nodes that connect the BBU
pool to F-RRH1 and F-RRH2. The F-RRH4 is assumed to be an MEC-enabled node
with a local control unit for connected F-RRHs which manages their inter-node
link communications. As such, from a network’s perspective, the resulting
objective in such MEC-enabled systems is to reduce the E2E delay in
communications between F-RRHs and the BBU pool. Note that the E2E delay
comprises of two components, i.e., communication and processing delays. If the
information from a connected F-RRH is processed by F-RRH4, the total delay can
be expressed as

$\mathcal{D}_{total}^{rrh}=\mathcal{T}_{C}^{rrh}+\mathcal{D}_{T}^{rrh},$ (1)

where $\mathcal{T}_{C}^{rrh}$ and $\mathcal{D}_{T}^{rrh}$ denote the
communication and processing delays, respectively, at F-RRH1. Likewise, if the
information received by F-RRH1 is forwarded to the BBU pool, the total delay
can be expressed as

$\mathcal{D}_{total}^{bbu}=\mathcal{T}_{C}^{bbu}+\mathcal{D}_{T}^{bbu},$ (2)

where $\mathcal{T}_{C}^{bbu}$ and $\mathcal{D}_{T}^{bbu}$ is the communication
and processing delay, respectively, at the BBU.

###  IV-B Hotspot

The hotspot is a situation when the number of active users in a given cell
increases beyond a threshold such that the additional users cannot be
supported by the BS. The hotspots occur during the large sporting and social
events in a geographical region. In hotspot situations, due to its fixed
architecture, the traditional C-RAN poses several physical and economic
limitations to install additional number of S-RRHs to meet increased traffic
demand. Hence, a cost-effective solution to this problem is to deploy the
F-RRHs. The deployment of F-RRHs in a hotspot region is controlled by the
control unit housed in the BBU pool. The decision for placement of F-RRHs in a
hotspot is taken by the BBU pool based on the current user density around the
S-RRH and corresponding resource (bandwidth) utilization. The BBU pool
regularly monitors the status of resource utilization in a cell for the
connected users. As soon as it goes beyond a predetermined threshold value, an
overload condition is detected at the S-RRH of the cell and the deployment of
F-RRHs gets initiated by the control unit at BBU pool. Note that, hereby, the
F-RRHs share the traffic load of additional users in the cell and offload the
overloaded S-RRH.

Fig. 3 further illustrates the offloading of users by the F-RRHs in hotspot
situations. Here, two different cells with individual S-RRHs controlled and
monitored by a shared BBU pool are depicted. The S-RRH3 in Cell 1 has a
standby platform with charging stations for F-RRHs to be deployed in hotspot.
Each of these F-RRHs is assumed to be equipped with a BS transceiver units,
and they are connected to the BBU pool via backhaul microwave links. The Cell
2 with S-RRH4 is considered as a hotspot. In an event when the control unit at
the BBU pool detects hotspot, the F-RRHs from Cell 1 are immediately engaged
at appropriate locations in Cell 2 to form small cells as shown in the figure.
The users lying in these small cells are offloaded from S-RRH4 and are served
by the corresponding F-RRHs. When the load at S-RRH4 reduces the engaged
F-RRHs are called back and placed again in the standby mode at S-RRH3. The
placement of F-RRHs in a given cell depends upon a utilization factor (UF) at
an S-RRH, which is calculated based on its current load and capacity as

$\ UF_{rrh}=\frac{L_{rrh}}{C_{rrh}}\times 100,$ (3)

where $L_{rrh}$ denotes the traffic load at the S-RRH generated due to
additional UEs and $C_{rrh}$ denotes the capacity in terms of bandwidth
available at the S-RRH.

###  IV-C Complex Terrains

In traditional C-RAN architecture, the cellular coverage over a large complex
terrain requires the installation of large number of S-RRHs interconnected via
a backhaul network. However, the deployment of the large number of S-RRHs is
costly over the complex terrains. In extreme terrains, i.e., marshes, lakes,
etc., the ground-based infrastructure may be even infeasible to install.
Further, in some terrains, e.g., hills, etc., the uneven elevations of
landscape cause LoS problem for reliable communications. Fig. 3 shows a
cooperative UC-RAN architecture where the cellular coverage to a complex
terrain is extended by the multiple inter-connected F-RRHs. Here, a number of
F-RRHs is deployed in small clusters over a geographical region. For
illustration purpose, a single cluster is shown in Fig. 3. Each such cluster
has an MEC-enabled F-RRH as cluster-head which has backhaul connection with a
BBU pool. The cluster-head is responsible for controlling and managing the
other F-RRHs in the respective cluster. Multi-hop may be rendered by various
F-RRHs in a cluster based on the cooperative relaying techniques. Furthermore,
to reduce the number of fronthaul links, the cluster of F-RRH relies upon the
extended-star topology.

##  V Performance evaluation of UC-RAN architecture

This section illustrates the performance of the proposed UC-RAN architecture
based on simulations using the LTE-TU Vienna simulator [19] and MATLAB. In
particular, the hotspot application scenario as given in Fig. 2 is considered
for the performance evaluation. The simulation parameter settings are given in
Table II. Here, a comparative performance analysis has been carried-out
between the macro BS, traditional C-RAN and UC-RAN architectures. Mainly, the
service latency, blocking probability, and total power consumption are
considered as performance metrics with respect to the variations in UEs’
traffic at Cell 2 and the handover traffic owing to UEs’ mobility from Cell 1
to Cell 2. Further, for evaluating the performance of macro BS-based
architecture, a macro BS is assumed to be located in Cell 2 with a coverage
area of $3$ km2. The traffic load is measured in terms of the number of
connected UEs to the BS. For this purpose, the number of connected UEs is
varied from $10\%$ to $100\%$ of the maximum number of UEs which can be
supported by the BS within available spectrum resources. The hotspot situation
in Cell 2 is created while increasing the number of handover UEs. Furthermore,
for the performance evaluation of traditional C-RAN architecture, an S-RRH and
a BBU pool are deployed with the same coverage area as defined for macro BS.
Also, the assignment of UEs to S-RRH is considered to be identical to that in
the macro BS-based architecture. Moreover, for the performance evaluation of
UC-RAN architecture, both the F-RRHs and S-RRHs are considered with the same
traffic load as in previous architectures.

TABLE II: Simulation parameters. Parameters | Value
---|---
LTE Bandwidth |  $20$ MHz
Number of UEs | 100-1000
Number of Macro-BS and S-RRHs | $2$
Number of UAVs | $4$
Number of BBU pool | 1
Transmit power of Macro BS and S-RRHs |  $43$ dBm
Transmit power of F-RRHs |  $30$ dBm
Modulation scheme |  $64$-QAM
Height of macro BS and S-RRHs |  $100$ ft
Altitude of F-RRHs |  $100$ ft

Fig. 4(a) plots the average E2E service delay versus number of UEs for various
RAN architectures. Here, it is observed that with the increased number of UEs,
the E2E average delay increases. This occurs due to the network congestion
problem resulting from the increased number of UEs within the available
spectrum resources at the BS in the considered RAN architectures. It is worth
mentioning that among the three RAN architectures, the UC-RAN has a better and
stable delay profile in hotspot application scenario even when the traffic
load approaches its maximum limit at BS. The main reason behind this is the
load sharing between F-RRH and S-RRH in UC-RAN as compared to an overloaded
S-RRH/macro BS in other architectures.

Figure 4:  Performance comparison of macro BS, C-RAN, and UC-RAN architectures
in terms of: (a) average E2E delay; (b) UE blocking probability; (c) total
power consumption.

Fig. 4(b) plots the UE blocking probability versus number of UEs for the three
RAN architectures. One can observe that the UE blocking probability is lowest
in the UC-RAN as compared to the other RAN architectures. Here also, the
improvement in UE blocking probability occurs due to the reduced load at F-RRH
and S-RRH under load sharing. Nevertheless, the nearly flat nature of UE
blocking probability curve for UC-RAN over a wide range of traffic load makes
it useful for next-generation cellular networks.

Fig. 4(c) plots the total power consumption versus number of UEs for the three
RAN architectures. It can be observed that the total power consumption in all
three architectures increases proportionally with the traffic load. Further,
the total power consumption in C-RAN and UC-RAN is approximately the same for
lower traffic loads. However, beyond the certain value of traffic load, the
power consumption in UC-RAN relatively increases as compared to C-RAN. It is
because at higher traffic loads, the power consumption at F-RRH significantly
increases in addition to that at S-RRH in UC-RAN architecture. Whereas in
C-RAN, the power consumption takes place only at S-RRH.

##  VI Conclusion

This article has proposed a UC-RAN architecture for beyond $5$G networks.
Especially, three application scenarios were considered, namely disaster
management, hotspot, and complex terrain deployment. The key implementation
issues along with the monitoring and control strategies of F-RRHs for UC-RAN
under the aforementioned application scenarios were discussed. Further, a
simulation-based comparative performance analysis was carried out for macro
BS, C-RAN, and UC-RAN architectures in terms of average E2E delay, UE blocking
probability, and total power consumption under the hotspot application
scenario. Compared with the other two RAN architectures, the UC-RAN was found
to be a promising candidate for future deployment based on its performance.

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