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# Collaborative Sensing in Perceptive Mobile Networks: Opportunities and
Challenges

Lei Xie, , S.H. Song, , Yonina C. Eldar, ,
and Khaled B. Letaief L. Xie and S.H. Song are with Department of Electronic
and Computer Engineering, the Hong Kong University of Science and Technology,
Hong Kong. e-mail: ($\\{$eelxie,eeshsong$\\}$@ust.hk).Yonina C. Eldar is with
the Faculty of Mathematics and Computer Science, Weizmann Institute of
Science, Rehovot 7610001, Israel (e-mail: yonina.eldar@weizmann.ac.il).Khaled
B. Letaief is with the Department of Electronic and Computer Engineering, the
Hong Kong University of Science and Technology, Hong Kong, and also with Peng
Cheng Laboratory, Shenzhen 518066, China (e-mail: eekhaled@ust.hk).

###### Abstract

With the development of innovative applications that demand accurate
environment information, e.g., autonomous driving, sensing becomes an
important requirement for future wireless networks. To this end, integrated
sensing and communication (ISAC) provides a promising platform to exploit the
synergy between sensing and communication, where perceptive mobile networks
(PMNs) were proposed to add accurate sensing capability to existing wireless
networks. The well-developed cellular networks offer exciting opportunities
for sensing, including large coverage, strong computation and communication
power, and most importantly networked sensing, where the perspectives from
multiple sensing nodes can be collaboratively utilized for sensing the same
target. However, PMNs also face big challenges such as the inherent
interference between sensing and communication, the complex sensing
environment, and the tracking of high-speed targets by cellular networks. This
paper provides a comprehensive review on the design of PMNs, covering the
popular network architectures, sensing protocols, standing research problems,
and available solutions. Several future research directions that are critical
for the development of PMNs are also discussed.

###### Index Terms:

Integrated sensing and communication, perceptive mobile networks, interference
management, networked sensing, environment estimation.

##  I Introduction

After several generations of development, wireless communications has evolved
from a system with only communication services to an intelligent network that
not only moves data but also performs edge-computing and distributed
learning/inference tasks [1]. The advancement of innovative applications such
as autonomous driving and environment monitoring further requires accurate
sensing capability from future wireless networks. To this end, the recently
proposed integrated sensing and communication (ISAC) framework offers a
promising way to integrate sensing and communication with possible hardware
and software reuse, especially after millimeter wave (mmWave) was adopted for
5G and beyond systems. Perceptive mobile networks (PMNs) are a special type of
ISAC system that focuses on adding sensing capability to the cellular networks
[2].

There are many favorable properties of cellular networks that can facilitate
sensing. First, the well-developed mobile network with high-density base
stations (BSs) can provide large sensing coverage. The high-density nodes are
very important because mmWave experiences severe pathloss and is thus not
suitable for long-range sensing tasks. Second, the large number of distributed
and connected sensing nodes enables networked sensing, where multiple
perspectives from different sensing nodes can be exploited to sense the same
target. Finally, the strong computation and communication power of PMNs create
a good platform for large-scale environment estimation and mapping, which will
not only benefit sensing but also enhance communication in terms of channel
estimation, resource allocation, beam tracking, and more.

However, there are also challenges faced by the design of PMNs [2]. Since PMNs
integrate sensing and communication in one system, interference management is
one of the most important issues to tackle. In particular, there exist three
types of interference. First, if the same node, e.g., a BS, is utilized for
transmitting sensing/communication signals and receiving radar echoes at the
same time, there will be self-interference (SI) [3]. Second, given both
communication and sensing users are served in the same frequency band, there
will be interference between the two sub-systems. Finally, prior information
about the environment is critical for sensing and normally obtained by
environment training (estimation). In conventional radar systems, the
transmitted signal in the environment training and target sensing periods is
the same, thus guaranteeing the same covariance structure for the clutter.
However, due to the high pathloss of mmWave, directional signals are utilized
for probing a target in the sensing period, but not in the training stage. As
a result, the sensing signal may cause different clutter covariance between
the training and sensing periods, which can be regarded as the interference to
environment estimation.

Besides interference management, the implementation of networked sensing and
environment estimation algorithms also faces several obstacles. On the one
hand, although networked sensing can take advantage of the multiple
perspectives from several sensing nodes, the collaborative sensing algorithm
must be computation and communication efficient due to the strict latency
requirement. On the other hand, the environment for networked sensing is much
more complex than traditional radar systems because the communication users,
the target and even the clutter patches in the environment may move. This
makes environment estimation very challenging, especially with distributed
sensing nodes. Furthermore, the stringent latency constraint precludes
estimation algorithms that require large amount of training data and heavy
computation.

This article aims to provide a comprehensive overview on the design of PMNs.
For that purpose, we will first introduce and compare several existing network
architectures and sensing protocols. In-depth discussions about the key
research problems will then be given, revealing the key design opportunities
and challenges in interference management, networked sensing and environment
estimation. Future research directions that are critical for the development
of PMNs and their service to other applications will also be covered.
Different from the existing reviews about ISAC [4, 5, 6, 3], this paper mainly
focuses on PMNs especially the benefits of the networked sensing.

##  II Network Architecture and Sensing Protocol

Network architecture and sensing protocol are the two most fundamental
frameworks for integrating sensing into current cellular networks.

Figure 1: Illustration of the PMN-TMT architecture. TABLE I: Comparison of existing ISAC networks. | Sensing Transmitter |  Sensing Receiver |  Full-Duplex |  Synchronization
---|---|---|---|---
Mono-static DFRC | BS |  BS |  Required |  Not Required
PMN-RRU | RRU |  RRU |  Not Required |  Required
PMN-TMT | BS |  TMT |  Not Required |  Required

###  II-A Network Architecture Design

There are three main network architectures proposed for PMNs in the
literature. In traditional cellular networks, interconnected BSs will serve
mobile user terminals (UEs). It is thus natural to select the BSs as the
transmitter for the sensing signal and the receiver for the echo signal [7].
This is referred to as the mono-static dual-function radar and communication
(DFRC) system, where the BSs are required to work in full-duplex (FD) mode for
transmitting and receiving signals at the same time. Another architecture [2]
integrates sensing into the cloud radio access network (C-RAN) where remote
radio units (RRUs) are densely distributed. To address the full-duplex issue,
some RRUs are selected to be the dedicated receivers in the downlink sensing
time, such that the sensing transmitter and receiver are separated to be
different RRUs. We will refer to this scheme as the PMN-RRU architecture.

A new PMN architecture was proposed in [8] where another layer of passive
target monitoring terminals (TMTs) are added to the conventional cellular
networks. TMTs are nodes designed for internet of things (IoT) applications
with only passive sensing functionalities, such as radar and vision [9]. They
are distributed in a target area and connected with the BSs through low
latency links. Given that TMTs will serve as dedicated radar receivers, BSs
only need to serve as the transmitter for sensing signals, thus saving the
need for full-duplex operation. We will refer to this design as the PMN-TMT
architecture.

Both PMN-RRU and PMN-TMT avoid the full-duplex operation at the cost of
network synchronization. Compared with RRUs, TMTs are low cost IoT devices
with only passive sensing functions that can also be utilized for other types
of IoT services. Fig. 1 shows an illustrative example of the PMN-TMT scheme
and Table I compares between the three architectures. Note that all three
architectures may perform collaborative or networked sensing but with
different sensing nodes, i.e., BSs, RRUs, and TMTs, respectively. In the
following, we will simply use TMTs as the sensing nodes and all discussions
apply to the other two architectures.

###  II-B Protocol Design

ISAC systems have inherent interference issues and proper protocols play the
key role in interference management and resource allocation between sensing
and communication [5]. In [4], the authors proposed a three-stage protocol to
coordinate communication and sensing modules for a DFRC system. In Stage 1,
the BS utilizes the omni-directional beam to search the sensing targets and
scatterers, and receives the uplink pilot from the UEs. In Stage 2, the BS
transmits the directional communication and sensing signals to the UEs and the
sensing target, respectively, where the transmission directions are estimated
in Stage 1. In Stage 3, the BS receives the radar echoes and the uplink
signals from the UEs for target detection and tracking. A two-stage protocol
was proposed in [10] to achieve joint target detection and channel estimation.
In the initial stage, the BS sends omni-directional downlink pilots to search
for the target, where the channel estimation is performed based on uplink
pilots from the UEs. Based on the initial results about the target and
communication scatterers obtained in the first stage, the BS sends directional
downlink pilots in the second stage for refined target detection and channel
estimation.

Figure 2: Frame structure for the two-stage sensing and communication protocol
[11].

To handle target sensing in complex environment with the presence of clutter,
the authors of [11] proposed a two-stage sensing protocol where the
communication signals are utilized to estimate the environment and sense the
target in two consecutive periods as illustrated in Fig. 2. In particular, the
downlink transmission time is divided into two periods, namely, the
environment estimation (EE) period and the target sensing (TS) period. In the
EE period, the BS transmits communication signals to serve the UEs, whose
echoes from the environment will be captured by the TMTs to estimate the
clutter covariance matrix. In the TS period, the BS transmits the designed
ISAC signal to serve the UEs and probe a target simultaneously111The sensing
signal is absent in the EE period and thus may change the environment
statistics.. TMTs receive the sensing echo (SE) reflected by the target and
clutter patches, and perform target sensing, where the clutter is suppressed
by exploiting the clutter covariance matrix estimated in the EE period. Note
that there are two interference management issues with this protocol: 1) the
interference between sensing and communication; 2) the ISAC signal should not
make the clutter covariance matrix in the TS period different from that in the
EE period. See next section for more details.

##  III Key Research Problems: Opportunities and Challenges

The design of PMN is still in its infancy. In the following, we identify
several key research problems and discuss the design challenges,
opportunities, and existing solutions.

###  III-A Interference Management

There are three types of interference in PMNs.

  * •

In DFRC, the BS needs to transmit and receive at the same time, causing SI.

  * •

As a special type of ISAC system, there is inherent interference between
sensing and communication in PMNs.

  * •

Due to the use of narrow beams in mmWave band, sensing signal toward a target
may change the environment statistics, and this can be regarded as
interference to environment estimation.

In the following, we discuss existing solutions for managing the above-
mentioned interference.

####  III-A1 Self Interference

Conventional pulse radar works in a half-duplex mode to avoid SI. In each
pulse repetition period, antennas will utilize a long time to receive the
potential echoes after transmitting the sensing signal. However, half-duplex
mode will not work for PMNs because BSs need to transmit communication signals
all the time. Among the three architectures mentioned above, the DFRC scheme
requires the BS to work in full-duplex mode to transmit and receive signals at
the same time. Under such circumstances, the SI is mitigated by self-
interference cancellation technique [12], which unfortunately is not very
mature. On the other hand, the PMN-RRU and PMN-TMT architectures naturally
avoid SI.

####  III-A2 Interference between Sensing and Communication

There are several unique features about sensing and communication signals in
PMNs: a) the sensing signals will interfere with the UEs and may degrade the
communication performance; b) the communication signals reflected by the
target can be utilized to probe the target as they are known by the BSs [7];
c) the communication signals reflected by the environment will cause clutter
for sensing. Depending on the design objective, different approaches have been
proposed to manage the interference between sensing and communication.

There are works that consider both sensing and communication performance.
Along this line, spectrum sharing between sensing and communication was
investigated in [13] by dynamic spectrum access and mutual interference
mitigation. In [8], the authors maximized the weighted average of sensing and
communication performance by jointly designing the transmitter and receiver,
where the sensing metric is selected as the signal-to-clutter-plus-noise ratio
(SCNR) and the communication metric is the signal-to-interference-plus-noise
ratio (SINR). An alternating optimization (AO) based framework is proposed to
iteratively update the transmit and receive beamformers. There are also works
that prioritize one side of the ISAC system. For example, the communication
performance was maximized in [14] by treating radar signals as interference.

In terms of the transceiver design, besides the AO-based method that jointly
optimizes the transmitter and receiver, linear transceivers have also been
considered to reduce the computation complexity. For example, zero-forcing
(ZF) and beam synthesis (B-syn) transmitter, and the minimum variance
distortionless response (MVDR) receiver were investigated in [8]. These linear
transceivers not only reduce the computation complexity but also provide
interesting physical insights. For example, “leaking” energy from
communication signals to the sensing target (ST)222Here, leaking energy from
communication signals means to revise the beamformer for the UEs so that part
of the communication signals will also be sent toward the ST. is more
efficient than forming a dedicated sensing signal, and the amount of energy
leaked from one UE to the ST depends on their channel correlation, which is
determined by their locations.

Figure 3: Comparison of the beam patterns for AO, B-syn and ZF.

AO, B-syn and ZF represent three different ways to tackle interference between
sensing and communication. In particular, AO allows interference between
sensing and communication and handles it by joint transceiver design, B-syn
only allows interference from communication to sensing and utilizes
communication signals as the sensing signal, while ZF eliminates all
interference between sensing and communication by designing a dedicated
sensing signal that will not interfere with the UEs. The beam patterns of AO,
B-syn, and ZF are illustrated in Fig. 3 where the interference between sensing
and communication is also illustrated.

Fig. 4 compares the sensing performance of the three transmitters with high
and low CR, respectively. It can be observed that, under both circumstances,
the performance of AO is the best, and B-syn outperforms ZF. Note that
different transceiver structures have different tolerance for interference,
and the stronger the orthogonality constraint (ZF$>$B-syn$>$AO), the worse the
sensing performance. Furthermore, when the CR is high, the gap between the
three schemes is smaller. This is partially due to the less energy left for
sensing when the CR is high. On the other hand, when the CR is low, high
interference is acceptable to the UEs, thus it is not necessary to completely
eliminate the interference from sensing to communication. However, high CR
forces AO to avoid the interference from sensing to communication like what
B-syn does. Therefore, the gap between AO and B-syn becomes smaller. When
their performance is comparable, B-syn is preferred because its computational
complexity is much lower than AO.

####  III-A3 Interference to Environment Estimation

Besides the interference between sensing and communication, the sensing signal
may also create interference for environment estimation. In particular, the
transmitted signal in the TS period is different from that in the EE period,
which may change the covariance structure of the clutter. To tackle this
issue, the sensing signal in the TS period must be designed to avoid
generating any echoes from the clutter patches, such that the covariance of
the clutter received by TMTs will be the same as that in the EE period. For
that purpose, the idea of B-syn can be adopted such that the sensing signal
has no energy toward the clutter patches.

Figure 4: Detection performance comparison between AO, B-syn and ZF under
different communication requirements (CR). The BS employs a ULA with
$N_{t}=16$ antennas. The receiver employs a hybrid beamforming structure with
$N_{r}=16$ antennas and $N_{RF}=4$ RF chains. The signal-to-noise ratio and
clutter-to-noise ratio are $17$dB and $40$dB, respectively.

###  III-B Networked Sensing

Networked sensing is one of the most exciting opportunities for PMN, where the
multiple perspectives from different TMTs can be collaboratively utilized to
improve sensing performance. The biggest advantage of networked sensing comes
from the macro-diversity of multiple TMTs. Note that, similar to wireless
communications, the macro-diversity gain improves the reliability of target
detection, because the chance that all the perspectives from different TMTs
are blocked is very low. However, the impact of the multiple antennas at one
TMT on sensing is totally different from that for wireless communication. The
influence of both the number of TMTs and the number of antennas per TMT was
analyzed in [11] with some interesting observations.

####  III-B1 Contribution of One Multi-antenna TMT

The contribution of a single multi-antenna TMT has several aspects. First, the
distance between the ST and the TMT affects the receive SNR exponentially due
to pathloss. Second, one TMT has its unique perspectives (AODs) toward the
target and the clutter patches in the environment. The relation between those
AODs dictates how easy it is for the TMT to separate the target from the
clutter patches. Finally, the number of antennas at one TMT affects sensing in
two aspects. On the one hand, the receive SNR at the TMT is directly
proportional to the number of antennas, which is referred to as the antenna
array gain. On the other hand, the number of antennas determines the array
resolution, which indicates the TMT’s ability to extract the target echo from
the clutter. In particular, arrays with larger number of antennas can separate
closer target and clutter patches.

However, multiple antennas no longer provide diversity gain for sensing. Note
that multi-antenna receivers achieve diversity gain in wireless communications
because the channel coefficients of different transmitter-receiver antenna
pairs are not fully correlated or even independent. However, in sensing, only
the line-of-sight (LoS) component is considered, and all non-LoS components
are regarded as part of the clutter. As a result, the channel between the ST
and the TMT becomes deterministic and no diversity can be achieved by the
multiple antennas of one TMT.

####  III-B2 Macro-diversity and TMT selection

Due to the different perspectives and independent reflecting coefficients, the
channels between the ST and different TMTs are normally independent. As a
result, multiple TMTs will provide macro-diversity gain for sensing. But, this
doesnot mean that we should include as many TMTs as possible, because the
detection probability is not a monotonic increasing function of the number of
TMTs. This phenomenon was analyzed and discussed in [11]. Assume there are
already $L$ TMTs participating in the networked sensing. A new TMT, i.e., the
($L$+1)-th TMT, will change the distributions of the decision statistic under
both the target-absence (TA) and target-presence (TP) hypotheses. Consider an
extreme case where the new target-TMT link is blocked. Under such
circumstances, the ($L$+1)-th TMT will only contribute noise under both
hypotheses, and thus cause worse performance. As a result, it is unnecessary
and even harmful to activate all TMTs to sense one target, making TMT
selection a critical task. In [11], a sufficient condition for the
contribution of one more TMT to be positive was derived, with which a TMT
selection algorithm was proposed.

###  III-C Environment Estimation

Another challenge for PMNs is EE. To achieve good sensing performance in the
presence of clutter, PMNs need to suppress the clutter by utilizing the prior
information about the environment obtained by EE. The performance of clutter
suppression will degrade if the clutter component of the signal-under-test
during the TS period has different statistical structure from that of the EE
period. A compressed sensing (CS)-based method was proposed in [15] to
estimate the spatial parameters and Doppler shift of the clutter.
Unfortunately, the computational cost of the CS-based method can be extremely
high, due to the continuous and rapidly-changing environment parameters in the
space and Doppler domains. This issue will be more serious when multiple
distributed TMTs are involved in the networked sensing where information
sharing between different TMTs is necessary. Thus, a computation and
communication efficient EE algorithm is desired.

To reduce the computation and communication workload for EE, the authors of
[11] proposed a distributed clutter covariance estimation algorithm where the
estimation is performed at TMTs. The low rank clutter in the mmWave band [13]
makes it possible to estimate the clutter covariance by using partial samples
of the received signal. However, the estimated covariance matrix may be ill-
conditioned due to the limited data samples. To this end, the EM-Net algorithm
was proposed by unfolding the expectation-maximization (EM) detector with
several learnable parameters, which achieves accurate estimation with less
data samples than existing methods.

Figure 5: Detection performance versus the batch size for different
environment estimation methods. The number of the antennas at one TMT and the
number of TMTs are set as $N_{R}=16$, and $L=3$, respectively.

Fig. 5 shows that EM-Net outperforms both the EM approach and the conventional
sample covariance matrix (SCM) based method, and the detection performance
will improve as the batch size for EE increases. However, the performance
improvement is achieved at the cost of larger latency and higher computation
workload.

##  IV Future Research Directions

In this section, we list several future research directions for the
development of PMNs.

###  IV-A Target Tracking

A big challenge for networked sensing is to track a moving target. This is
because, different from traditional radar, the coverage of one TMT is very
limited. Thus, the sensing task needs to be smoothly handed over to another
TMT when the target is moving. The situation is even worse for networked
sensing where the handover is not from one TMT to another TMT, but from one
group of TMTs to another group with overlapping between two groups. Note that
handover is not a new problem, as it is also required for wireless
communication services. However, the problem is not the same for sensing due
to several reasons. First, the synchronization requirement for networked
sensing makes the problem much more difficult. Second, the handover criteria
are more complex. Specifically, besides the signal strength which is normally
the criterion for handover in communication, angles (AOA/AOD) and moving
direction also matter a lot for sensing. Finally, if the target is moving
fast, the narrow sensing beams may have difficulty following the target so
that “predictive” sensing beams may be required.

To this end, besides the Kalman filter (KF) based method, machine learning or
data-driven approaches may play a more important role due to several reasons.
On the one hand, the physical environment, e.g., the roads, for fast moving
target is relatively fixed, making it easier to extract useful information
from history data. On the other hand, the strong computation and communication
power of PMNs make it possible to build a map of the environment, i.e.,
Simultaneous Localization and Mapping (SLAM). Furthermore, graph neural
networks (GNNs) have been shown effective to handle wireless network design
problems, such as traffic prediction, resource allocation and data detection,
and the handover problem can also be formulated as a graph optimization
problem.

###  IV-B Joint Networked and Individual Sensing

Networked sensing can be utilized to facilitate the design of many smart
applications. For example, in autonomous driving, a key task for the vehicle
is to understand the environment, including the road, other vehicles,
pedestrians, etc. Currently, this is mainly achieved by many sensors installed
on the vehicle, such as vision, lidar, and radar. We will refer to this as
individual sensing. Networked sensing by PMNs can help measure the position
and velocity of a moving vehicle. Due to latency requirement, such results can
not be directly utilized to control the vehicle. However, networked sensing
may provide useful complement for individual sensing in autonomous driving
applications.

There are several promising research directions. First, networked sensing is
able to reduce the workload of individual sensing. For example, networked
sensing can build up and keep updating a map about the static environment,
saving the need for individual sensing to construct such a map. Second,
networked sensing will be able to monitor the changing environment, e.g.,
other moving vehicles, to provide assistant information for individual
sensing. Finally, many long-term tasks such as routing and traffic management
can be taken over by networked sensing.

###  IV-C Intelligent Reflecting Surfaces (IRS)-aided Sensing

Compared with the sub-6GHz band, mmWave experiences high pathloss which makes
directional transmission by spatial beamforming inevitable. As a result, both
mmWave sensing and communication rely heavily on the LoS link, which
unfortunately can be easily blocked. Intelligent reflecting surfaces (IRSs)
have been proposed to create an alternative link between the transmitter and
receiver, and attracted much attention in wireless communication design. The
ability IRSs in creating semi-LoS links can be utilized to facilitate several
aspects of PMN design including interference management, networked sensing,
and velocity estimation. In particular, due to the complex environment, it is
possible that the sensing target has the same AoA/AoD as some UEs with respect
to one TMT. Under such circumstance, IRSs will be able to create another link
and avoid the interference between sensing and communication users. The same
idea can be utilized for networked sensing, when one TMT’s perspective to the
target is blocked. In fact, with proper phase-shift design, one IRS can help
multiple TMTs.

The application of IRS in sensing is not limited to creating alternative
paths. The additional perspective can also help improve velocity estimation.
For example, with the conventional mono-static radar, only the radial
projection of the true velocity can be estimated due to the nature of Doppler
effect. As a result, if the target is moving on the direction perpendicular to
the line connecting the target and one TMT, the velocity estimation will not
be accurate. The additional path provided by the IRS can provide another
perspective to observe the target, which is useful to recover the true
velocity.

###  IV-D Joint Active and Passive Sensing

The above discussions considered networked sensing in an active manner, i.e.,
the BS actively transmits the sensing signal and TMTs perform the detection
based on the echoes. The active mode works for any targets. However, if the
PMN wants to locate the UEs which also use the communication service, there is
another mode of passive sensing. In particular, the UEs will transmit signals
in the uplink period, which can also be used for radar detection. For targets
with weak electromagnetic wave reflection characteristics, e.g., pedestrians,
the passive method can provide a good detection performance. The passive mode
is also more power-efficient as there is no transmission overhead. However, it
loses several advantages of active sensing, such as the well-designed
waveform, flexible transmitting beam, higher detection range, etc. As a
result, it may not be sufficient to only utilize the passive mode to achieve
accurate sensing performance. A joint design between active and passive
sensing in PMNs is an interesting direction to investigate, which is
especially useful for sensing-aided communication.

###  IV-E Sensing-aided Communication

By far, we have been focusing on the design of PMNs to achieve sensing by
exploiting the well-developed communication network. On the other hand,
sensing can also be utilized to assist communication especially in the mmWave
band where highly directional signals are transmitted in a sparse channel.

####  IV-E1 Sensing-aided Channel Estimation

The transmission of highly directional beams over a sparse channel creates
challenging problems for channel estimation, because of the very large
searching space in the angular domain plus the limited measurements. Among
other solutions, the CS-based methods were widely studied to exploit the
sparse structure, but they suffer from high computational complexity and are
not robust to noise, hardware-led errors in array response, and the off-grid
issues. To this end, the sensing results by PMNs can be of great assistance.
In particular, the sparse channel is composed of several main scatterers whose
locations can be obtained by sensing. As a result, sensing results can
significantly reduce the searching space for channel estimation and improve
the performance with limited measurements.

####  IV-E2 Sensing-aided Beam Tracking

Beam alignment is a fundamental issue in mmWave communication. The narrow
mmWave beams are very sensitive to the change of environment, e.g., the
movement of the UEs. As a result, beam alignment becomes more difficult in
highly mobile scenarios, as the state of a UE can change before beam training
has been completed. Thus, under certain circumstance, “predictive” beams are
required to maintain good communication performance. To this end, the powerful
capability of sensing in location and velocity estimation will be able to help
for tracking the moving UE and forming communication beams.

##  V Conclusion

Networked sensing brings unprecedented opportunities to exploit the well-
developed infrastructure of cellular networks for sensing purposes, but at the
same time faces serious challenges in interference management and environment
estimation. Joint processing among distributed nodes over the network also
incurs difficulties in designing communication and computation-efficient
algorithms. Existing network architectures, sensing protocols, and transceiver
design could tackle some of the challenges while achieving favorable results
such as the macro-diversity from multiple sensing nodes, the array gain by
multiple receive antennas, and the efficient environment estimation with data-
driven methods. However, the development of ISAC/PMNs is in its infancy and
there are still many obstacles to conquer before we can fully enjoy the
synergy between sensing and communication to support more innovative
applications.

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