# Ambient IoT: A missing link in 3GPP IoT Devices Landscape

M. Majid Butt, Nitin R. Mangalvedhe, Nuno K. Pratas, Johannes Harrebek, John
Kimionis, Muhammad Tayyab, Oana-Elena Barbu, Rapeepat Ratasuk, and Benny
Vejlgaard M. Majid Butt, Nitin R. Mangalvedhe and Rapeepat Ratasuk are with
Nokia Standards, Naperville, USA. email:{majid.butt, nitin.mangalvedhe,
<EMAIL_ADDRESS>K. Pratas, Johannes Harrebek, Oana-Elena
Barbu, and Benny Vejlgaard are with Nokia Standards, Aalborg, Denmark. email:
{nuno.kiilerich_pratas, johannes.harrebek, oana-elena.barbu,
<EMAIL_ADDRESS>John Kimionis is with Nokia Bell Labs, 600
Mountain Ave, Murray Hill, NJ, 07974, USA (e-mail: ioannis.kimionis@nokia-
bell-labs.com.)Muhammad Tayyab is with Nokia Standards, Oulu, Finland.
(e-mail<EMAIL_ADDRESS>
###### Abstract

Ambient internet of things (IoT) is the network of devices which harvest
energy from ambient sources for powering their communication. After decades of
research on operation of these devices, Third Generation Partnership Project
(3GPP) has started discussing energy harvesting technology in cellular
networks to support massive deployment of IoT devices at low operational cost.
This article provides a timely update on 3GPP studies on ambient energy
harvesting devices including device types, use cases, key requirements, and
related design challenges. Supported by link budget analysis for
backscattering energy harvesting devices, which are a key component of this
study, we provide insight on system design and show how this technology will
require a new system design approach as compared to New Radio (NR) system
design in 5G.

###### Index Terms:

Energy harvesting, ambient IoT, backscattering, 3GPP.

##  I Introduction

The internet of things (IoT) market is a major source of revenue for future
wireless technology. Moving beyond enhancement of the mobile broadband devices
market that is supported by fourth-generation (4G) cellular technology, ultra
reliable low-latency communication (URLLC) and machine type communication
(MTC) service classes were also introduced in fifth-generation (5G) cellular
technology, which target use cases requiring high reliability and massive
deployment of smaller and cheaper IoT devices, respectively. Sixth-generation
(6G) technology will further expand these market segments. The IoT market is
expected to steadily grow from 2020 to 2025, and further increase is expected
beyond that period at a higher rate. Building and commercial, health,
agriculture, infrastructure, and industries are some of the key sectors
targeted by the IoT market. Almost all sectors are forecasted to grow in the
future, but infrastructure and health segments are expected to grow at higher
rates in the future compared to others [1].

Due to the large number of IoT devices that are expected to be deployed,
growth of the IoT market requires future networks to be energy efficient and
sustainable to make a business case. Energy harvesting technology is key to
the success of the IoT market as it can considerably reduce device operational
cost. Energy harvesting devices are ’battery free’ devices, which harvest
energy from natural or ambient sources, e.g., electromagnetic, solar, thermal,
pressure etc., and operate either with a small battery or without battery and
do not require battery replenishment. They are also termed as ’zero energy’
devices due to their capability to operate without a dedicated power source
[2].

Energy harvesting communication has been widely discussed in research for more
than a decade, e.g., see [3, 4] and references therein. Recent literature
outlines key challenges and complexity to power massive number of IoT devices
[5, 6] using radio frequency (RF) signals, but this is the first time 3GPP has
started discussing energy harvesting and backscattering as a potential IoT
technology for future networks in various study groups [7, 8]. Fundamental
aspects of the technology are well researched, but feasibility of the
technology in devices under 3GPP networks is still unknown under realistic
network operating conditions and business model.

The goal of this article is to articulate the main reasons behind industry
interest in energy harvesting as a future technology and discuss design
considerations and solutions. As 3GPP has never before discussed energy
harvesting devices, also called ambient IoT (A-IoT) devices, there are several
challenges to be addressed before energy harvesting technology is adopted in
future networks. This article provides a comprehensive overview addressing
three key questions:

  * •

How will energy harvesting IoT technology complement existing 3GPP IoT
technologies?

  * •

What are the key use cases and the associated design requirements for the
technology?

  * •

What are the challenges associated both with technology as well as its
standardization in 3GPP?

Section II discusses the first question by describing existing 3GPP IoT
technologies and how energy harvesting technology fills the technology gap.
Section III addresses the second question by reviewing recent status of 3GPP
discussions on energy harvesting technology and the way forward, while Section
IV and Section V attempt to answer the third question by focusing on link-
level analysis and the main technology challenges. Finally, we conclude with
the main findings of the article in Section VI.

Figure 1: Cellular IoT Technology landscape with major technologies covering
different potential use cases.

##  II IoT SPACE IN 3GPP

The expansion of IoT to encompass use cases in new verticals has steadily
progressed from the initial applications in second-generation (2G) cellular
technology to the current 5G technologies. IoT use cases span a range of
requirements in terms of cost, complexity, delay constraints, and other
metrics. Fig. 1 depicts the division of IoT based on requirements into the
following classes.
Broadband IoT: This class of IoT comprises one extreme of IoT applications
that require high data rates along with a low latency. The use cases covered
by this class of IoT include fleet management, industry gateways, video, hot-
spots, wearables, and industrial wireless sensors. The requirements can be
served by 4G Long Term Evolution (LTE) as well as 5G New Radio (NR)
technologies (using enhanced Mobile Broadband, or eMBB, and Reduced Capability
or RedCap, devices). RedCap devices have a lower cost and complexity than NR
eMBB devices, making them more attractive for many IoT applications.
Critical IoT: This class of IoT constitutes another extreme of IoT
applications requiring ultra-high reliability and ultra-low latency. Typical
use cases for this class include factory automation, industrial control,
robotics, and Augmented Reality (AR) or Virtual Reality (VR). The LTE High-
Reliability Low-Latency Communication (HRLLC) feature of LTE, introduced in
Release 15, and the NR URLLC technologies feature of NR, also introduced in
Release 15, can serve these applications.
Massive IoT: This class of IoT consists of delay-tolerant applications where
the requirements include low-cost devices with low energy consumption,
extended network coverage, and support of a massive number of devices. Example
use cases include fleet management, asset tracking, smart meters, smart city,
gateways, sensors, voice, and point-of-sales. LTE for Machines (LTE-M) and
Narrowband IoT (NB-IoT) technologies fulfill the requirements for these
applications [9].
Ambient IoT: The last class is the latest addition and comprises the very low
end of IoT use cases where the requirements include ultra-low complexity
devices, ultra-low power consumption, and small form factor, which can be met
by devices that are either battery-less or have the capability for limited
energy storage. The requirements related to power consumption and device
cost/complexity cannot be met by existing cellular technologies. Therefore,
new technologies under NR in Rel-19 or later and 6G are needed.

Fig. 1 also shows some key performance indicators (KPIs) for the different
cellular IoT technologies [10]. It can be seen that in terms of peak data
rate, broadband IoT is at the high end whereas massive IoT is at the low end.
Among the legacy classes, in terms of reliability and latency, KPIs for
critical IoT are the most stringent whereas those for massive IoT are the most
relaxed. A-IoT is expected to have even more relaxed KPIs.

##  III A-IoT Use Cases and Requirements

###  III-A Technology Overview

Figure 2: Ambient IoT landscape with both energy harvesting and backscattering
devices. Ambient energy sources like sun, TV signals and Wifi signals can be
used for energy harvesting and excitation as well.

A-IoT technology includes both energy harvesting devices with active
transmission as well as passive backscattering devices. Backscattering devices
do not have an active transmission component and modulate information on the
received signal from the exciter where the exciter (often used interchangeably
with the terms illuminator or activator) can be any node generating RF
signals. On the other hand, energy harvesting with active transmission and
small storage allows higher range and better quality of service (QoS) as
compared to backscattering devices without any storage. Different use cases
can be envisioned for both technologies, where low-cost backscattering
technology is more suitable to use cases such as asset tracking and
monitoring, livestock, etc., while high-end use cases requiring better QoS can
be supported by energy harvesting technology with active transmission. Both
backscattering and active energy-harvesting devices can be battery-less or
carry a small battery.

Fig. 2 shows an overview of broad coverage of A-IoT technology. Backscattering
IoT devices can be used both in monostatic and bi-/multi-static
configurations, where the term monostatic is used when both the energy exciter
and the reader functionalities are performed by the same device, while
bistatic deployment specifies the scenario where the energy exciter and the
reader are physically two different devices. Fig. 2 illustrates that 3GPP base
stations (BSs, i.e., gNB or dedicated micro cells) and smartphones can be used
both as exciters, and readers, or RF energy source, while an ambient energy
source (e.g., TV, WiFi signals) can also be used for excitation for
backscattering as well as energy source for energy harvesting for active
transmissions. Besides RF signals, energy harvesting devices can make use of
other ambient energy sources like sun light, pressure, thermal etc, to harvest
and store energy and use it to make data transmission. Though 3GPP focus is
more on RF energy harvesting devices in A-IoT studies, devices powered by
other energy sources, e.g., solar which has several orders of magnitude high
power density as compared to RF source, are not precluded in this study.”

###  III-B 3GPP Focus Areas and Use Cases

3GPP has recently studied A-IoT in both Service and System Aspects working
group 1 (SA1) [8] and Radio Access Networks (RAN) plenary [11]. The 3GPP work
was initiated by the Technical Report in TR 22.840 [8] by SA1 to capture use
cases, traffic scenarios, device constraints of ambient power enabled IoT; and
identify new potential service requirements as well as new KPIs.

Considering the limited size and complexity affordable by practical
applications for battery-less devices with no energy storage capability or
devices with limited energy storage that do not need to be replaced or
recharged manually, the output power of the energy harvester typically ranges
from 1 $\mu$W to a few hundreds of $\mu$W. Existing cellular devices may not
work well with energy harvesting due to their peak power consumption of higher
than 10 mW.

The RAN plenary later agreed to study A-IoT radio use cases and requirements
in [11]. This study has completed in 3GPP and a summary of main agreements is
captured in this section.

3GPP RAN has agreed to study four deployment topologies for A-IoT devices:

  1. 1.

BS $\leftrightarrow$ A-IoT device

  2. 2.

BS $\leftrightarrow$ Intermediate node $\leftrightarrow$ A-IoT device

  3. 3.

BS $\leftrightarrow$ Assisting node UE $\leftrightarrow$ A-IoT device
$\leftrightarrow$ BS

  4. 4.

UE $\leftrightarrow$ A-IoT device

In Topology 1, the A-IoT device directly and bidirectionally communicates with
a base station. In Topology 2, the A-IoT device communicates bidirectionally
with an intermediate node between the device and the base station. The
intermediate node can be a relay, Integrated Access and Backhaul (IAB) node,
user equipment (UE), repeater, etc., which can support A-IoT. In Topology 3,
the A-IoT device transmits data/signaling to a base station and receives
data/signaling from the assisting node; or the A-IoT device receives
data/signaling from a base station and transmits data/signaling to the
assisting node. In this topology, the assisting node can be a relay, IAB, UE,
repeater, etc. which can support A-IoT. In Topology 4, the A-IoT device
communicates bidirectionally with a UE. 3GPP SA1 has defined a large set of
use cases for ambient power-enabled IoT and 3GPP RAN is defining
representative deployment scenarios for studies, each covering more use cases
and topologies.

Using bi-/multi-static links can enable positioning and will remove the
challenges associated with full duplexing self-interference for a monostatic
A-IoT illuminator and reader device.

The 3GPP RAN study assumes three A-IoT device types:

  * •

Device A: (Passive) Pure battery-less devices with no energy storage
capability at all, no independent signal generation/amplification (i.e.,
capable of only backscattering), and completely dependent on the availability
of an external source of energy.

  * •

Device B: (Semi-Passive) Devices with limited energy storage capability that
do not need to be replaced or recharged manually, no independent signal
generation but backscattering potentially with reflection gain.

  * •

Device C (Active) Actively transmitting device with limited energy storage
capabilities based on ambient energy sources.

Figure 3: Design targets for A-IoT devices as discussed in 3GPP.

###  III-C Design Targets for Cellular AIoT Devices

Figure 4: UE illumination scenarios with different receive options for device
type A and B.

The main design target pillars in focus for 3GPP A-IoT are depicted in Fig. 3.
The target is low data rate, ultra-low cost, ultra-low-power devices at small
form factor but with improved range compared to Radio Frequency Identification
(RFID) and with positioning enabled at relaxed accuracy.

The 3GPP RAN study assumes some high-level design targets for the A-IoT
devices as outlined below.
Device complexity:

  * •

For Device A, the complexity target is to be comparable to ultrahigh frequency
(UHF) RFID ISO18000-6C (EPC C1G2).

  * •

For Device B, the target is such that: Device A complexity $<$ Device B
complexity $<$ Device C complexity.

  * •

For Device C, the complexity target is to be orders-of-magnitude lower than
NB-IoT.

Device power consumption:

  * •

For Device A, the power consumption target during transmitting/receiving is
$\leq$ 1 $\mu$W.

  * •

For Device B, the target during transmitting/receiving is such that:

    * –

Device A power consumption $<<$ Device B power consumption $<$ Device C power
consumption; or

    * –

Device A power consumption $\leq$ Device B power consumption $<$ Device C
power consumption.

  * •

For Device C, the device power consumption is $\leq$ 1 mW.

Device data rates:
Maximum supported data rate not less than 5 kbps and minimum supported data
rate not less than 0.1 kbps.
Positioning accuracy (SA1 study based):

  * •

Indoor: 3m at 90$\%$

  * •

Outdoor: several 10m at $90\%$

Based on these simple design targets the ambition is to standardize radio
interface and protocols to enable deployment of such Ambient IoT devices. 3GPP
is expected to study A-IoT devices in Rel. 19 with the aim to standardize in
later releases. It is largely expected that AIoT technology will be one of the
key bridge topics in 3GPP for 6G.

##  IV Feasibility Studies

As 3GPP studies are heavily focused on backscattering A-IoT devices, we
discuss some insights on range coverage aspects for Devices A and B in this
section. Through numerical evaluation on link budget for both forward and
reverse links, we show which topologies are more practical, when taking into
account the device type, transmit power level, and fixed vs mobile deployment.
The received power $P_{\rm rx,tag}$ at the A-IoT device from a given
transmitter is computed by product of two factors A and B such that $P_{\rm
rx,tag}=AB$, where $A=P_{T}G_{T}/d_{1}^{\gamma}$ and $B=G_{\rm
tag}(\lambda/4\pi)^{2}$ [12]. $P_{\rm rx,tag}$ is the received power at a tag,
$P_{T}$ is output power from a transmitter, $G_{T}$ is transmit antenna gain,
$G_{\rm tag}$ is the tag antenna gain, $\lambda$ is the carrier wavelength,
$d_{1}$ is the transmitter to tag distance and $\gamma$ is the path loss
exponent ($\gamma=2$ for free space, $\gamma=3$ for fading environment).
Similarly, at the reader, the received backscatter signal power $P_{\rm rx,r}$
is computed by $P_{\rm rx,r}=AB^{2}G_{R}M/d_{2}^{\gamma}$, where $G_{R}$ is
the receive antenna gain, $M$ is the backscatter tag modulation factor, and
$d_{2}$ is the tag to receiver distance. Note that the above formula is
generalized and applies to all backscatter deployment scenarios. For a
monostatic case, it is imperative that $d_{1}=d_{2}$ and $G_{T}$ may be equal
to $G_{R}$. For a bistatic case where the transmitter and receiver use equal
antenna gain (e.g., two access points) the formula can be simplified by
setting $G_{T}=G_{R}$. For a bistatic case where different equipment is used
for transmitting and receiving (e.g. a UE and a BS), all parameters can be set
individually.

Considering a tag performing binary modulation with a bit rate of 10 kbps and
a target (uncoded) bit error rate (BER) = 1$\%$, the required backscatter
signal to noise ratio (SNR) at the Rx is ${\rm SNR}_{\min}=4.3$ dB. Assuming a
15 kHz signal bandwidth $W$, a receiver noise figure $NF=6$ dB, a
shadowing/fade margin of $F=10$ dB, the reader reference sensitivity has a
value of approximately $S=-112$ dBm (i.e., using
$S=-174+NF+F+10\log_{10}W+\rm{SNR}_{\min}$).

For battery-less devices that harvest RF energy (Device A), two conditions
must be met for successful communication:

  1. 1.

Received excitation power at the tag $P_{\rm{tag}}$ must exceed a certain
power-up threshold $P_{\rm thr}$ in order for the tag to ”wake up”. In the
following calculations, a threshold of $P_{\rm{thr}}=-19$ $\rm{dBm}$ is
assumed (common value for passive RFID devices). This typically governs the
”max operating range”.

  2. 2.

Received backscattered power at the receiver must exceed the reader
sensitivity requirement $S$ ($S$ = -112 dBm in the following calculations).

Three different bistatic use cases with UE excitation are depicted in Fig. 4
with some example communication ranges. The path loss exponent $\gamma$ is
assumed to be 3 for outdoor cases and 2.5 for indoor cases. For passive A-IoT
devices (Device A), the modulation factor is $M$=0.25 and for semi-passive
A-IoT devices (Device B) $M$=1. Note that all devices are assumed to have an
omni-directional antenna ($G_{\rm{tag}}$ = 2 dBi), which represents a baseline
scenario. In the first case from the left, excitation of devices is performed
in an area by a UE and reception is performed by a fixed BS. The middle case
involves indoor small-cell deployment with excitation of devices is performed
by a UE and reception by a small-cell access point. The case to the right
involves indoor small-cell deployment with device excitation and reading by
two different UEs.

It is interesting to note that for bistatic cases, the two links’ (exciter
(Tx)-to-device and device-to-receiver (Rx)) maximum range depends on each
other. For a short Tx-device distance, a significantly higher device-Rx
distance can be achieved. This has also been shown experimentally in [13]. The
opposite is also true for semi-passive devices (Device B), but not for passive
A-IoT devices that need to be energized by the exciter. Fig. 5(a,b) shows this
contrast for the UE excitation-gNB reception case, where a yellow shaded
region corresponds to successful communication operation. The Figure 5(a)
corresponds to an A-IoT device (Device A), for which the Tx-device distance is
very limited, since the device power-up threshold must be exceeded. Despite
that fact, very long device-Rx ranges can be achieved (several hundreds of
meters) for a sufficiently sensitive receiver. Fig. 5(b) corresponds to an
energy-assisted backscattering device (Device B). Since there is no RF power-
up constraint, the total operating region is larger compared to Device A.
Keeping a short Tx-device range allows for exponentially larger device-Rx
range and vice versa. Similar trends are observed for the case of UE exciter
and indoor gNB reception (Fig. 5(c) and Fig. 5(d)).

Fig. 4 provides example numbers for the feasibility of each deployment (RF
harvesting devices (Device A) and energy-assisted devices (Device B)). In
general, Device B can afford longer Tx-device ranges than Device A, due to the
relaxed power-up requirements, and therefore can be used for larger area
coverage. For UE excitation cases, where the UE can be brought to the
proximity of the devices, both Device A and Device B can be used, and the
uplink range from the device to a base station can reach 300 meters, which can
accommodate similar inter-site distances (ISD).

Considering the above link budgets, the topology of illuminator and receiver
to be deployed will depend on application and the environment. However, it is
clear that for large scale outdoor or enterprise deployments, UE illumination
will be favorable, since the power source can be brought close to the AIoT
device. Then uplink ranges of several hundred meters can be achieved, and the
AIoT device backscattering can be received by fixed infrastructure gNBs.

Figure 5: Operating regions (Yellow indicates sufficient received SNR) (a) for
UE-device-gNB scenario device A operation (left) is governed by power-up
threshold (b) for UE-device-gNB scenario device B operation (right) is
receiver-sensitivity limited (c) for UE-device-indoor gNB scenario device A
operation (left) is governed by power-up threshold (d) for UE-device-indoor
gNB scenario device B operation (right) is receiver-sensitivity limited.

##  V A-IoT: TECHNOLOGY CHALLENGES AND SOLUTIONS

###  V-A A-IoT Device Access Techniques

Energy harvesting IoT devices have the following key difference as compared to
NR UE and associated challenges must be resolved before deploying A-IoT
technology.

  1. 1.

New Terminal States: A major consideration for the access techniques in energy
harvesting communication is the uncertainty in the availability of energy.
Unlike conventional communication scenarios, availability of ambient energy is
not stable, and it varies spatially as well. When designing access techniques,
this uncertainty has to be considered [14]. For energy harvesting devices with
active transmission (Device C), duty cycle design, scheduling decisions and
wakeup occasions need to be designed based on availability of intermittent
energy. As devices are not always connected with the network, NR functionality
based on radio resource control (RRC) states (active/idle) may not be valid
and we may need new device state definitions.

  2. 2.

Device Registration:  NR UEs are registered with the network and a context is
maintained in the network. As A-IoT devices are low-cost devices with small
form factor, it will be difficult to register them with the network through
subscriber identity module (SIM). However, it is important to establish a
simplified form of A-IoT device identification in a 3GPP network and 3GPP SA2
studies will look into this aspect. It is not yet clear how A-IoT device
identification will be managed, i.e., through Subscription Permanent
Identifier (SUPI) by the network or through application-defined IDs managed by
a 3GPP network.

  3. 3.

Mobility Tracking: Typical use cases targeted by AIoT technology, e.g.,
inventory tracking, may not require continuous mobility management as in NR.
Mobility management requires maintaining context of UE in the network and
preparing handover when UEs move between cells. As some of the target use
cases do not require this functionality, this overhead can be avoided by
finding solutions for ’on demand’ mobility management.

  4. 4.

Link Budget Analysis: Backscattering devices are low-cost devices and
typically first harvest some energy to activate their circuit and then
backscatter information. Thus, 3GPP system design not only involves
backscattering link budget analysis but illuminator-to-backscattering device
analysis as well.

  5. 5.

Distributed Energy Sources: As shown in Section IV, due to a large link budget
requirement, gNB is not the best exciter for backscattering devices. In 3GPP
networks, all the NR UE access mechanisms are handled by gNB. For
backscattering devices, we need to rely on distributed low-complexity 3GPP
network illuminators, readers and/or smartphones, which require more
coordination between network devices to service A-IoT devices and demand for
new network protocol design.

  6. 6.

Simplified Protocols: Due to small form factor and low-cost requirement, A-IoT
devices cannot support full stack access protocol. Thus, simplified protocol
design is a key requirement for such devices, particularly backscattering
devices which depend on strong activation signals. Access and security
mechanisms need a new design approach that provides required authentication
and security to backscattering devices at lower complexity and energy cost.
Access and security protocol complexity may depend on each device type
discussed in 3GPP.

###  V-B A-IoT Device Positioning Techniques

Figure 6: AIoT positioning incorporating various network entities. UE to UE
links (PC5 interface) and UE to gNB links (Uu interface) have been marked.

For several 3GPP A-IoT deployment scenarios and use cases, support for
positioning is a key feature. Fig. 6 shows an example of an A-IoT deployment
based on multi-static backscattering with positioning capabilities. The
positioning of A-IoT devices can be based on the existing 3GPP positioning
architecture, using a 3GPP node to trigger the event and one or more 3GPP
nodes to listen to a given reference signal, e.g., another gNB or Positioning
Reference Unit (PRU). The positioning session can be controlled and monitored
by the location management function (LMF). A positioning session can be
enabled by a UE in the network or from the network side. Sidelink can be
another means to configure UEs in proximity of the A-IoT device as activation
exciter and/or as positioning anchors.

A-IoT positioning encompasses two complementary use-cases: 1) locate the A-IoT
device and 2) use the A-IoT device to locate other devices.

In the first use case, the A-IoT device is often used for tracking objects in
production, shipping, etc., and therefore, it is necessary for the NR network
to locate the A-IoT device. However, since the A-IoT activation and reading
ranges and device processing capabilities are severely limited, the typical
time- and angle-based NR positioning techniques cannot be straightforwardly
applied. Specifically, A-IoT systems do not lend themselves well to downlink
positioning techniques, since these requires the A-IoT device to be
synchronized to the network and be able to detect positioning signals and
extract relevant measurements (differential timing measurements, angle of
departures, carrier phases, etc.), requirements which are impossible to
fulfill by most A-IoT devices. Round-trip time (RTT) methods pose an even more
challenging task, since the A-IoT device must, in addition to processing
downlink, also reply with a paired uplink transmission. Given the above, the
only feasible alternative remains uplink positioning, where the
Transmission/Reception Points (TRPs) are tasked with detecting the device and
measuring the required positioning metrics, conditioned on the availability
and proximity of radios to charge and activate the A-IoT device.

In the second use-case, the A-IoT device may itself be used to locate other
devices (other A-IoT devices or NR UEs). Specifically, NR localization of a UE
requires signaling from multiple TRPs with known location, to multilaterate
the UE location. When an insufficient number of TRPs are available and/or in
poor line-of-sight (e.g., very often in indoor scenarios), cheap A-IoT devices
may take the role of the TRP, and become the so-called A-IoT TRP. However, the
location of an A-IoT TRP:

  1. 1.

is typically unknown to the NR network right after deployment. That is because
such devices are manually installed at physical locations associated with
areas which have positioning blind spots.

  2. 2.

may change over time. The devices may be manually moved around to cover
other/new positioning blind spots e.g., where such blind spots are created
when an existing TRP gets obstructed due to temporary blockage. For example, a
TRP can be blocked in an indoor factory when large packages are moved around
the factory or new equipment is installed.

  3. 3.

cannot be computed using NR positioning techniques due to the limited range of
such devices. While it has high cost-reduction potential, it is not
straightforward how to efficiently utilize an A-IoT device as a TRP, since the
current NR positioning protocols are not equipped to compute and track the
position of the A-IoT device.

###  V-C A-IoT Authentication and Security

Similar to RFID, security methods for A-IoT systems must guarantee at least
(a) authentication, i.e., confirming device identity and (b) confidentiality,
i.e., ensure that the A-IoT device is not being eavesdropped on. NR security
mechanisms guarantee both requirements, data integrity and availability via
higher layer protocols and complex cryptography. The complexity of NR security
is however both a blessing (for existing services) and a curse for A-IoT
systems. A-IoT systems consist of devices that are extremely limited in terms
of computational power, that can rarely support basic cryptography. Therefore,
there is a need to develop new security protocols and methods tailored to
A-IoT design limitations. The above-mentioned protocols may instead resort to
physical layer security mechanisms. Physical layer security is built on
exploiting the uniqueness of the propagation channel responses between the
A-IoT device and other legitimate radios comprising the A-IoT system and
determining a transmission scheme tailored to those specific conditions only.

###  V-D Spectrum

The spectrum to be targeted for A-IoT should be common across as many regions
as possible and with the same or similar spectrum access regulations, both to
ensure the ease of mobility of AIoT devices across different regions as well
as to simplify the standardization of the A-IoT ecosystem. For example, RFID
technology can be deployed across different bands, where the actual band
depends on the use case and the requirements [15]. However, in order to avoid
coexistence issues with legacy RFID technologies and at the same time take
advantage of the existing cellular deployments, the A-IoT system should target
licensed frequency bands where cellular technologies already operate as well
as the unlicensed bands at 5 GHz and 6 GHz. In particular, the unlicensed
bands will have both infrastructure-based and sidelink-based access
technologies allowing for the coordination of the A-IoT communications and aid
RF-based charging of the A-IoT devices.

##  VI Conclusion

This article provides a comprehensive overview of Ambient IoT technology in
5G. Ambient IoT is new technology on top of existing cellular IoT technology
and targets market segment below the existing technologies. Battery
replenishment and cost is major hurdle in deployment of IoT technology and
A-IoT can help fill this gap by targeting reduced complexity and cost. Before
this technology becomes a reality, several challenges need to be overcome
including development of suitable access, transmission, positioning, and radio
resource management techniques, identifying spectrum and topologies for
various scenarios, and incorporating low complexity security protocols. This
article summarizes 3GPP discussions on A-IoT studies and envision an ambient
IoT technology that will make future networks more sustainable and ready for
massive deployment of IoT devices.

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