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

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