12 research outputs found
Sensing as a Service in 6G Perceptive Mobile Networks: Architecture, Advances, and the Road Ahead
Sensing-as-a-service is anticipated to be the core feature of 6G perceptive
mobile networks (PMN), where high-precision real-time sensing will become an
inherent capability rather than being an auxiliary function as before. With the
proliferation of wireless connected devices, resource allocation in terms of
the users' specific quality-of-service (QoS) requirements plays a pivotal role
to enhance the interference management ability and resource utilization
efficiency. In this article, we comprehensively introduce the concept of
sensing service in PMN, including the types of tasks, the
distinctions/advantages compared to conventional networks, and the definitions
of sensing QoS. Subsequently, we provide a unified RA framework in
sensing-centric PMN and elaborate on the unique challenges. Furthermore, we
present a typical case study named "communication-assisted sensing" and
evaluate the performance trade-off between sensing and communication procedure.
Finally, we shed light on several open problems and opportunities deserving
further investigation in the future
Sensing With Random Signals
Radar systems typically employ well-designed deterministic signals for target
sensing. In contrast to that, integrated sensing and communications (ISAC)
systems have to use random signals to convey useful information, potentially
causing sensing performance degradation. This paper analyzes the sensing
performance via random ISAC signals over a multi-antenna system. Towards this
end, we define a new sensing performance metric, namely, ergodic linear minimum
mean square error (ELMMSE), which characterizes the estimation error averaged
over the randomness of ISAC signals. Then, we investigate a data-dependent
precoding scheme to minimize the ELMMSE, which attains the {optimized} sensing
performance at the price of high computational complexity. To reduce the
complexity, we present an alternative data-independent precoding scheme and
propose a stochastic gradient projection (SGP) algorithm for ELMMSE
minimization, which can be trained offline by locally generated signal samples.
Finally, we demonstrate the superiority of the proposed methods by simulations.Comment: 6 pages, 5 figures, submitted to ICASSP 202
Sensing as a Service in 6G Perceptive Networks: A Unified Framework for ISAC Resource Allocation
In the upcoming next-generation (5G-Advanced and 6G) wireless networks,
sensing as a service will play a more important role than ever before.
Recently, the concept of perceptive network is proposed as a paradigm shift
that provides sensing and communication (S&C) services simultaneously. This
type of technology is typically referred to as Integrated Sensing and
Communications (ISAC). In this paper, we propose the concept of sensing quality
of service (QoS) in terms of diverse applications. Specifically, the
probability of detection, the Cramer-Rao bound (CRB) for parameter estimation
and the posterior CRB for moving target indication are employed to measure the
sensing QoS for detection, localization, and tracking, respectively. Then, we
establish a unified framework for ISAC resource allocation, where the fairness
and the comprehensiveness optimization criteria are considered for the
aforementioned sensing services. The proposed schemes can flexibly allocate the
limited power and bandwidth resources according to both S&C QoSs. Finally, we
study the performance trade-off between S&C services in different resource
allocation schemes by numerical simulations
Communication-Assisted Sensing in 6G Networks
The exploration of coordination gain achieved through the synergy of sensing
and communication (S&C) functions plays a vital role in improving the
performance of integrated sensing and communication systems. This paper focuses
on the optimal waveform design for communication-assisted sensing (CAS) systems
within the context of 6G perceptive networks. In the CAS process, the base
station actively senses the targets through device-free wireless sensing and
simultaneously transmits the pertinent information to end-users. In our
research, we establish a CAS framework grounded in the principles of
rate-distortion theory and the source-channel separation theorem (SCT) in lossy
data transmission. This framework provides a comprehensive understanding of the
interplay between distortion, coding rate, and channel capacity. The purpose of
waveform design is to minimize the sensing distortion at the user end while
adhering to the SCT and power budget constraints. In the context of target
response matrix estimation, we propose two distinct waveform strategies: the
separated S&C and dual-functional waveform schemes. In the former strategy, we
develop a simple one-dimensional search algorithm, shedding light on a notable
power allocation tradeoff between the S&C waveform. In the latter scheme, we
conceive a heuristic mutual information optimization algorithm for the general
case, alongside a modified gradient projection algorithm tailored for the
scenarios with independent sensing sub-channels. Additionally, we identify the
presence of both subspace tradeoff and water-filling tradeoff. Finally, we
validate the effectiveness of the proposed algorithms through numerical
simulations
Waveform Design for Communication-Assisted Sensing in 6G Perceptive Networks
The integrated sensing and communication (ISAC) technique has the potential
to achieve coordination gain by exploiting the mutual assistance between
sensing and communication (S&C) functions. While the sensing-assisted
communications (SAC) technology has been extensively studied for high-mobility
scenarios, the communication-assisted sensing (CAS) counterpart remains widely
unexplored. This paper presents a waveform design framework for CAS in 6G
perceptive networks, aiming to attain an optimal sensing quality of service
(QoS) at the user after the target's parameters successively ``pass-through''
the SC channels. In particular, a pair of transmission schemes, namely,
separated S&C and dual-functional waveform designs, are proposed to optimize
the sensing QoS under the constraints of the rate-distortion and power budget.
The first scheme reveals a power allocation trade-off, while the latter
presents a water-filling trade-off. Numerical results demonstrate the
effectiveness of the proposed algorithms, where the dual-functional scheme
exhibits approximately 12% performance gain compared to its separated waveform
design counterpart
Integrated Sensing and Communications: Recent Advances and Ten Open Challenges
It is anticipated that integrated sensing and communications (ISAC) would be
one of the key enablers of next-generation wireless networks (such as beyond 5G
(B5G) and 6G) for supporting a variety of emerging applications. In this paper,
we provide a comprehensive review of the recent advances in ISAC systems, with
a particular focus on their foundations, system design, networking aspects and
ISAC applications. Furthermore, we discuss the corresponding open questions of
the above that emerged in each issue. Hence, we commence with the information
theory of sensing and communications (SC), followed by the
information-theoretic limits of ISAC systems by shedding light on the
fundamental performance metrics. Next, we discuss their clock synchronization
and phase offset problems, the associated Pareto-optimal signaling strategies,
as well as the associated super-resolution ISAC system design. Moreover, we
envision that ISAC ushers in a paradigm shift for the future cellular networks
relying on network sensing, transforming the classic cellular architecture,
cross-layer resource management methods, and transmission protocols. In ISAC
applications, we further highlight the security and privacy issues of wireless
sensing. Finally, we close by studying the recent advances in a representative
ISAC use case, namely the multi-object multi-task (MOMT) recognition problem
using wireless signals.Comment: 26 pages, 22 figures, resubmitted to IEEE Journal. Appreciation for
the outstanding contributions of coauthors in the paper
Joint Beamforming Design for Dual-Functional MIMO Radar and Communication Systems Guaranteeing Physical Layer Security
The dual-functional radar and communication (DFRC) technique constitutes a promising next-generation wireless solution, due to its benefits in terms of power consumption, physical hardware, and spectrum exploitation. In this paper, we propose sophisticated beamforming designs for multi-user DFRC systems by additionally taking the physical layer security (PLS) into account. We show that appropriately designed radar waveforms can also act as the traditional artificial noise conceived for drowning out the eavesdropping channel and for attaining increased design degrees of freedom (DoF). The joint beamforming design is formulated as a non-convex optimization problem for striking a compelling trade-off amongst the conflicting design objectives of radar transmit beampattern, communication quality of service (QoS), and the PLS level. Then, we propose a semidefinite relaxation (SDR)-based algorithm and a reduced-complexity version to tackle the non-convexity, where the globally optimal solutions are found. Moreover, a robust beamforming method is also developed for considering realistic imperfect channel state information (CSI) knowledge. Finally, simulation results are provided for corroborating our theoretical results and show the proposed methods' superiority