572 research outputs found
Low-Range-Sidelobe Waveform Design for MIMO-OFDM ISAC Systems
Integrated sensing and communication (ISAC) is a promising technology in
future wireless systems owing to its efficient hardware and spectrum
utilization. In this paper, we consider a multi-input multi-output (MIMO)
orthogonal frequency division multiplexing (OFDM) ISAC system and propose a
novel waveform design to provide better radar ranging performance by taking
range sidelobe suppression into consideration. In specific, we aim to design
MIMO-OFDM dual-function waveform to minimize its integrated sidelobe level
(ISL) while satisfying the quality of service (QoS) requirements of multi-user
communications and the transmit power constraint. To achieve a lower ISL, the
symbol-level precoding (SLP) technique is employed to fully exploit the degrees
of freedom (DoFs) of the waveform design in both temporal and spatial domains.
An efficient algorithm utilizing majorization-minimization (MM) framework is
developed to solve the non-convex waveform design problem. Simulation results
reveal radar ranging performance improvement and demonstrate the benefits of
the proposed SLP-based low-range-sidelobe waveform design in ISAC systems
Cooperative Cell-Free ISAC Networks: Joint BS Mode Selection and Beamforming Design
Owing to the promising ability of saving hardware cost and spectrum
resources, integrated sensing and communication (ISAC) is regarded as a
revolutionary technology for future sixth-generation (6G) networks. The
mono-static ISAC systems considered in most of existing works can only achieve
limited sensing performance due to the single observation angle and easily
blocked transmission links, which motivates researchers to investigate
cooperative ISAC networks. In order to further improve the degrees of freedom
(DoFs) of cooperative ISAC networks, the transmitter-receiver selection, i.e.,
base station (BS) mode selection problem, is meaningful to be studied. However,
to our best knowledge, this crucial problem has not been extensively studied in
existing works. In this paper, we consider the joint BS mode selection,
transmit beamforming, and receive filter design for cooperative cell-free ISAC
networks, where multi-BSs cooperatively serve communication users and detect
targets. We aim to maximize the sum of sensing
signal-to-interference-plus-noise ratio (SINR) under the communication SINR
requirements, total power budget, and constraints on the numbers of
transmit/receive BSs. An efficient joint beamforming design algorithm and three
different heuristic BS mode selection methods are proposed to solve this
non-convex NP-hard problem. Simulation results demonstrates the advantages of
cooperative ISAC networks, the importance of BS mode selection, and the
effectiveness of our proposed algorithms
Partially Distributed Beamforming Design for RIS-Aided Cell-Free Networks
Cell-free networks are regarded as a promising technology to meet higher rate
requirements for beyond fifth-generation (5G) communications. Most works on
cell-free networks focus on either fully centralized beamforming to maximally
enhance system performance, or fully distributed beamforming to avoid extensive
channel state information (CSI) exchange among access points (APs). In order to
achieve both network capacity improvement and CSI exchange reduction, we
propose a partially distributed beamforming design algorithm for reconfigurable
intelligent surface (RIS)-aided cell-free networks. We aim at maximizing the
weighted sum-rate of all users by designing active and passive beamforming
subject to transmit power constraints of APs and unit-modulus constraints of
RIS elements. The weighted sum-rate maximization problem is first transformed
into an equivalent weighted sum-mean-square-error (sum-MSE) minimization
problem, and then alternating optimization (AO) approach is adopted to
iteratively design active and passive beamformer. Specifically, active
beamforming vectors are obtained by local APs and passive beamforming vector is
optimized by central processing unit (CPU). Numerical results not only
illustrate the proposed partially distributed algorithm achieves the remarkable
performance improvement compared with conventional local beamforming methods,
but also further show the considerable potential of deploying RIS in cell-free
networks.Comment: 5 pages, 4 figures, accepted by TV
A Novel Joint Angle-Range-Velocity Estimation Method for MIMO-OFDM ISAC Systems
Integrated sensing and communications (ISAC) is emerging as a key technique
for next-generation wireless systems. In order to expedite the practical
implementation of ISAC within pervasive mobile networks, it is essential to
equip widely-deployed base stations with radar sensing capabilities. Thus, the
utilization of standardized multiple-input multiple-output (MIMO) orthogonal
frequency division multiplexing (OFDM) hardware architectures and waveforms
becomes pivotal for realizing seamless integration of effective communication
and sensing functionalities. In this paper, we introduce a novel joint
angle-range-velocity estimation algorithm for the MIMO-OFDM ISAC system. This
approach exclusively depends on conventional MIMO-OFDM communication waveforms,
which are widely adopted in wireless communications. Specifically, the
angle-range-velocity information of potential targets is jointly extracted by
utilizing all the received echo signals within a coherent processing interval
(CPI). Therefore, the proposed joint estimation algorithm can achieve larger
processing gains and higher resolution by fully exploiting echo signals and
jointly estimating the angle-range-velocity information. Theoretical analysis
for maximum unambiguous range, resolution, and processing gains are provided to
verify the advantages of the proposed joint estimation algorithm. Finally,
extensive numerical experiments are presented to demonstrate that the proposed
joint estimation approach can achieve significantly lower
root-mean-square-error (RMSE) of angle/range/velocity estimation for both
single-target and multi-target scenarios.Comment: 13 pages, 8 figures, submitted to IEEE Tran
End-to-End Learning for Symbol-Level Precoding and Detection with Adaptive Modulation
Conventional symbol-level precoding (SLP) designs assume fixed modulations
and detection rules at the receivers for simplifying the transmit precoding
optimizations, which greatly limits the flexibility of SLP and the
communication quality-of-service (QoS). To overcome the performance bottleneck
of these approaches, in this letter we propose an end-to-end learning based
approach to jointly optimize the modulation orders, the transmit precoding and
the receive detection for an SLP communication system. A neural network
composed of the modulation order prediction (MOP-NN) module and the
symbol-level precoding and detection (SLPD-NN) module is developed to solve
this mathematically intractable problem. Simulations verify the notable
performance improvement brought by the proposed end-to-end learning approach.Comment: 5 pages, 4 figures, accepted by WC
Cramer-Rao Bound Optimization for Active RIS-Empowered ISAC Systems
Integrated sensing and communication (ISAC), which simultaneously performs
sensing and communication functions using the same frequency band and hardware
platform, has emerged as a promising technology for future wireless systems.
However, the weak echo signal received by the low-sensitivity ISAC receiver
severely limits the sensing performance. Active reconfigurable intelligent
surface (RIS) has become a prospective solution by situationally manipulating
the wireless propagations and amplifying the signals. In this paper, we
investigate the deployment of active RIS-empowered ISAC systems to enhance
radar echo signal quality as well as communication performance. In particular,
we focus on the joint design of the base station (BS) transmit precoding and
the active RIS reflection beamforming to optimize the parameter estimation
performance in terms of Cramer-Rao bound (CRB) subject to the service users'
signal-to-interference-plus-noise ratio (SINR) requirements. An efficient
algorithm based on block coordinate descent (BCD), semidefinite relaxation
(SDR), and majorization-minimization (MM) is proposed to solve the formulated
challenging non-convex problem. Finally, simulation results validate the
effectiveness of the developed algorithm and the potential of employing active
RIS in ISAC systems to enhance direct-of-arrival (DoA) estimation performance.Comment: 30 pages, 9 figures, submitted to IEEE journa
Joint Beamforming Design for RIS-Assisted Integrated Sensing and Communication Systems
Integrated sensing and communication (ISAC) has been envisioned as a
promising technology to tackle the spectrum congestion problem for future
networks. In this correspondence, we investigate to deploy a reconfigurable
intelligent surface (RIS) in an ISAC system for achieving better performance.
In particular, a multi-antenna base station (BS) simultaneously serves multiple
single-antenna users with the assistance of a RIS and detects potential
targets. The active beamforming of the BS and the passive beamforming of the
RIS are jointly optimized to maximize the achievable sum-rate of the
communication users while satisfying the constraint of beampattern similarity
for radar sensing, the restriction of the RIS, and the transmit power budget.
An efficient alternating algorithm based on the fractional programming (FP),
majorization-minimization (MM), and manifold optimization methods is developed
to convert the resulting non-convex optimization problem into two solvable
sub-problems and iteratively solve them. Simulation studies illustrate the
advancement of deploying RIS in ISAC systems and the effectiveness of the
proposed algorithm.Comment: Accepted by IEEE TV
Joint Sensing and Communication Optimization in Target-Mounted STARS-Assisted Vehicular Networks: A MADRL Approach
The utilization of integrated sensing and communication (ISAC) technology has
the potential to enhance the communication performance of road side units
(RSUs) through the active sensing of target vehicles. Furthermore, installing a
simultaneous transmitting and reflecting surface (STARS) on the target vehicle
can provide an extra boost to the reflection of the echo signal, thereby
improving the communication quality for in-vehicle users. However, the design
of this target-mounted STARS system exhibits significant challenges, such as
limited information sharing and distributed STARS control. In this paper, we
propose an end-to-end multi-agent deep reinforcement learning (MADRL) framework
to tackle the challenges of joint sensing and communication optimization in the
considered target-mounted STARS assisted vehicle networks. By deploying agents
on both RSU and vehicle, the MADRL framework enables RSU and vehicle to perform
beam prediction and STARS pre-configuration using their respective local
information. To ensure efficient and stable learning for continuous
decision-making, we employ the multi-agent soft actor critic (MASAC) algorithm
and the multi-agent proximal policy optimization (MAPPO) algorithm on the
proposed MADRL framework. Extensive experimental results confirm the
effectiveness of our proposed MADRL framework in improving both sensing and
communication performance through the utilization of target-mounted STARS.
Finally, we conduct a comparative analysis and comparison of the two proposed
algorithms under various environmental conditions
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