2026· IEEE Transactions on Communications· Vol 74, pp. 12671-12687· 0 citations· 60 references
Abstract
Integrated sensing and communication (ISAC) under a cell-free (CF) architecture enables seamless connectivity and sensing coverage by allowing multiple distributed access points (APs) to jointly serve users and detect targets, thereby mitigating cell-edge effects and enhancing spatial diversity. However, wideband CF-ISAC also suffers from frequency-selective fading and strong inter-AP interference. To address these challenges, we investigate a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted ISAC framework, which extends full-space coverage and mitigates multiplicative fading and blockage effects. A joint optimization strategy is developed to maximize the weighted ISAC joint rate by jointly optimizing bandwidth and power allocation, receive beamforming, and active STAR-RIS beamforming. To tackle the non-convexity caused by variable coupling and intricate constraints, an efficient alternating optimization algorithm is developed. The original problem is decomposed into several subproblems: first, a closed-form solution for receive beamforming is derived; next, the resource allocation semi-analytical solutions are obtained via Karush-Kuhn-Tucker (KKT) conditions. Subsequently, the active STAR-RIS coefficients are optimized by capitalizing on fractional programming and majorization-minimization (MM) techniques. Finally, simulation results reveal that the proposed scheme achieves a 20.34% weighted ISAC joint-rate gain over the passive scheme, validating its effectiveness in wideband CF-ISAC systems.
This paper investigates a downlink integrated sensing and communication (ISAC) system utilizing non-orthogonal multiple access (NOMA), empowered by an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) operating in energy splitting mode. We maximize the communication sum rate subject to per-user quality-of-service constraints, target sensing requirements, and practical active STAR-RIS hardware constraints. The proposed formulation is further generalized to a unified framework covering active/passive STAR-RIS architectures and NOMA/space-division multiple access schemes. To solve the resulting highly non-convex problem, we develop a computationally efficient optimization framework that alternately optimizes the base station transmit beamforming and STAR-RIS beamforming by introducing a common set of auxiliary variables, thereby accelerating convergence in solving the subproblems. We also develop a worst-case robust design under norm-bounded channel state information (CSI) uncertainty, where the uncertain rate expressions are replaced with tractable conservative bounds. Simulation results show that the proposed algorithm converges much faster than fractional programming-based benchmarks, and that the active STAR-RIS assisted NOMA achieves the best performance under various system constraints. The results also demonstrate the resilience of the proposed robust design against CSI errors, while revealing that excessive active amplification may degrade the achievable sum rate under practical nonlinear amplifier distortion.
Noureen Khan, Muhammad Rehman, Jinho Choi et al.· IEEE Transactions on Wireles...· 0 citations
Integrated Sensing and Communication (ISAC) is emerging as a key technology for next-generation wireless networks, enabling simultaneous communication and sensing functionalities. This paper focuses a RIS-assisted full-duplex (FD) ISAC system, in which a multi-antenna base station (BS) concurrently performs multi-user uplink and downlink transmission while also carrying out radar sensing. To maximize the joint uplink–downlink sum rate, an optimization problem is formulated under practical constraints, such as radar detection SINR, self-interference, BS transmit power, user power budgets, and RIS unit-modulus conditions. To address the nonconvexity of this problem, a two-stage hybrid optimization approach is developed. In the first stage, the augmented Lagrangian technique decomposes the complex problem into simpler subproblems involving beamforming, power allocation, and RIS phase optimization, leading to a feasible initial solution. The second stage employs a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework to refine this solution adaptively, enabling the system to respond effectively to variations in the channel environment, mobility patterns, and interference levels. The proposed hybrid framework achieves optimal resource allocation while maintaining feasibility, robustness, and adaptability. Analytical results confirm its convergence behavior, and extensive simulation results confirm that the proposed scheme consistently outperforms conventional optimization and single-agent DRL baselines in sum-rate maximization, interference mitigation, and sensing accuracy, confirming its effectiveness for RIS-assisted full-duplex ISAC systems.
S. Waqas, Fenghua Huang, Fakhar Abbas et al.· IEEE Transactions on Wireles...· 0 citations
In integrated sensing and communication (ISAC) systems, stringent sensing performance constraints can severely limit the power available for communication. Hybrid reconfigurable intelligent surfaces (HRISs) with capabilities of both passive reflection and active signal amplification can significantly improve communication performance in the power-limited regime. This motivates us to analyze and optimize the performance of an HRIS-aided multiple-input-multiple-output (mMIMO) ISAC system. We first estimate the effective uplink/downlink channels using the minimum mean square error method. We then derive closed-form expressions for the communication sum-rate and sensing Cram\'er-Rao lower bound (CRLB). It is shown that under the equal power allocation strategy, the CRLB remains independent of the HRIS coefficients. Then, we formulate a joint optimization problem of power allocation and HRIS beamforming to maximize the communication sum-rate while ensuring specified sensing CRLB constraints. To solve the formulated non-convex problem, we propose an alternating optimization algorithm based on fractional programming and successive convex approximation. Extensive simulations validate our analysis and proposed algorithm, showing significant improvements in both communication and sensing performances enabled by the HRIS. For example, an HRIS with only $4$ active elements offers $97.30\%$ improvement in the communication sum-rate, while ensuring a sensing CRLB constraint of $-30$ dB.
Smriti Uniyal, Tian-Yu Fang, M. di Renzo et al.· 0 citations
This letter investigates the sensing-centric design of reconfigurable intelligent surface (RIS)-enabled rate-splitting multiple access-integrated sensing and communication (RSMA-ISAC) systems. Specifically, we propose a new beam-gain approximation method to enhance the sensing beam gain while satisfying communication quality-of-service (QoS) constraints. Since the joint optimization of the beamforming vectors and RIS phase shifts is highly coupled and non-convex, existing methods typically rely on generic optimization solvers involving substantial computational complexity. To address this issue, we propose an efficient constraints-separation-based alternating optimization algorithm (CS-AO). Our proposed algorithm effectively decouples the optimization variables and yields closed-form solutions for all subproblems, thereby significantly reducing the computational burden. Simulation results show that the proposed algorithm achieves sensing beam-gain performance comparable to successive convex approximation (SCA) and semidefinite relaxation (SDR) benchmarks, while achieving more than 120-fold and 50-fold runtime reductions. In addition, compared with conventional space-division multiple access (SDMA) schemes, the proposed design exhibits substantial sensing beam gain.
Xue-Jun Cheng, Qian Zhang, Y. Jiao et al.· IEEE Wireless Communications...· 0 citations
The integration of reconfigurable intelligent surface (RIS) technology with cell-free massive MIMO (CF mMIMO) enhances wireless network sum-rate performance. This paper proposes a novel segmented RIS-assisted CF mMIMO architecture for downlink transmission, where RIS elements within each segment share a common reflection coefficient to reduce optimization complexity. A weighted sum-rate maximization problem is formulated by jointly optimizing AP beamforming, segment-level RIS coefficients, and RIS element-to-segment association under perfect and imperfect CSI. For perfect CSI, the problem is reformulated via fractional programming and decomposed into subproblems solved by FP-CVX, FP-SCA, and swap matching. For imperfect CSI, an Minimum Mean Square Error–Discrete Fourier Transform (MMSE–DFT)-based channel estimation method is adopted with the same alternating optimization framework. A Joint Beamforming and Segmentation Parameter Optimization (JBSPO) algorithm is developed for the resulting mixed-integer non-convex problem. The framework is extended to finite backhaul capacity, phase-dependent amplitude response, discrete phase shifts, and spatially correlated channels. To capture the impact of spatial correlation on RIS segmentation, a new element-level grouping suitability indicator is introduced. Simulation results show that the proposed PCS-RIS achieves a performance–complexity trade-off, outperforming fixed segmentation, random phase shift, and no-RIS schemes. It improves WSR by about 1.21%, 1.41%, and 1.58% over PCFS-RIS, random, and no-RIS, respectively, with only 0.97% gap to ideal RIS, while reducing runtime by up to 48.98%.
Jian Zhu, Lei Feng, F. Zhou et al.· IEEE Transactions on Communi...· 0 citations
A cross-layer end-to-end (E2E) resource orchestration framework for green CF-mMIMO ISAC systems with distributed multi-target detection is developed and a fundamental implementation trade-off is revealed: FIS provides lower detector-processing complexity and higher detection performance, whereas PIS substantially reduces fronthaul requirements.
Z. Behdad, Ozlem Tuugfe Demir, Ki Won Sung et al.· arXiv.org· 0 citations
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