This letter investigates a multi-cell reconfigurable intelligent surface (RIS)-assisted cloud radio access network (Cloud-RAN), where multiple remote radio heads (RRHs) are connected to a central unit (CU) via limited-capacity fronthaul links, and multiple user equipments (UEs) in each cell are supported by dedicated RISs. In particular, we provide a unified comparative analysis of three RIS architectures—active RIS, passive RIS with continuous phase shifts, and passive RIS with discrete phase shifts—highlighting their fundamental performance trade-offs under practical fronthaul and RIS structural constraints. To mitigate the effects of fronthaul limitations and inter-cell interference, we propose a joint optimization framework for fronthaul compression, coordinated transmit power allocation, and RIS beamforming to maximize the system sum-rate. Numerical results verify that the proposed approach effectively balances fronthaul resource constraints and RIS configuration flexibility, providing key insights into the performance trade-offs of different RIS architectures for practical network deployments.
Integrated sensing, communication, and computing (ISCC) technology significantly improves spectrum efficiency and reduces hardware costs by unifying the functionalities of sensing, communication, and computation. However, the degradation of wireless link quality caused by obstacles may lead to severe offloading latency. This paper investigates the application of reconfigurable intelligent surface (RIS) technology in ISCC network to enhance the reliability of wireless links and improve the efficiency of computation offloading. In the proposed network, integrated sensing and communication (ISAC) devices employ the same hardware and signaling mechanisms to perform both sensing and communication tasks, while mobile edge computing (MEC) technology is leveraged to process sensing data. To address the cross-layer resource management problem, we formulate a total latency minimization problem for both communication and computation under sensing accuracy constraints, by jointly optimizing the waveform precoding design matrix of ISAC devices, the beam pattern scaling factor, the RIS’s reflective beamforming, and the computing frequency of edge server (ES). Since this problem is highly non-convex, we propose an iterative algorithm based on block coordinate descent (BCD) framework, which leverages semi-definite relaxation (SDR) method, the Charnes-Cooper transform (CCT) method, and closed-form solution to alternately optimize three variable blocks until convergence is achieved for the final solution. Extensive simulations validate the effectiveness of the proposed scheme, demonstrating that the RIS-assisted joint optimization scheme significantly reduces the total system latency. Moreover, we reveal the trade-off between sensing accuracy and system latency.
Yingsheng Peng, Jinbei Zhang, Jingpu Duan et al.· IEEE Transactions on Green C...· 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
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
This paper investigates the integration of active beyond-diagonal reconfigurable intelligent surfaces (BD-RIS) into dual-functional radar-communication (DFRC) systems for multi-user sixth generation (6G) networks. Unlike conventional passive or diagonal RIS, the proposed architecture employs active non-reciprocal impedance networks that enable joint amplitude and phase control, thereby mitigating multiplicative fading and enabling direction-dependent wave manipulation. We formulate a joint optimization problem to maximize the weighted sum rate (WSR) while preserving radar probing capability, subject to base-station transmit power and RIS amplification constraints. To solve the resulting non-convex problem, we develop an alternating optimization framework combining weighted minimum mean-squared error (WMMSE)-based precoder design, fractional programming for active BD-RIS beamforming, and quasi-Newton projection to enforce group-wise amplification constraints. The computational complexity of the proposed algorithm is rigorously characterized, highlighting the scalability benefits of group-connected BD-RIS architectures. Extensive simulations demonstrate up to 2.8 bps/Hz WSR improvement over passive diagonal RIS, 41.75% transmit power reduction compared with active diagonal RIS, and strong robustness under imperfect channel state information and hardware impairments. Furthermore, the proposed architecture achieves superior radar beampattern control and favorable energy efficiency-performance trade-offs, establishing active BD-RIS as a promising enabler for sensing-aware, energy-efficient, and high-capacity 6G DFRC networks.
Bittu Mishra, Keshav Singh, Chih-Peng Li et al.· IEEE Open Journal of the Com...· 0 citations
Reconfigurable intelligent surfaces (RISs) can effectively enhance wireless coverage, yet a single RIS may be insufficient in blocked multi-user scenarios. This paper investigates a distributed multi-RIS-assisted downlink NOMA network with two representative transmission schemes. In comprehensive RIS-assisted NOMA (CRN), multiple RISs jointly assist transmission. In selective RIS-assisted NOMA (SRN), only the most suitable RIS is activated. Unlike idealized multi-RIS models, the proposed framework incorporates transmitter/receiver hardware impairments, RIS phase errors modeled by the von Mises distribution, imperfect channel state information (ipCSI), and imperfect successive interference cancellation (ipSIC). Under independent but not identically distributed Nakagami- $m$ cascaded channels, the end-to-end gains of CRN and SRN are characterized by moment-based Gamma approximations, from which tractable closed-form approximations for outage probability (OP) and ergodic capacity (EC) are derived. High-SNR asymptotic expressions and diversity-order results are also obtained, showing that imperfect CSI/SIC leads to OP floors, whereas practical impairments impose finite capacity ceilings at high SNR. An overhead-aware net energy-efficiency model is further developed by accounting for channel estimation, feedback/configuration, inter-RIS coordination, and RIS-selection overheads. Numerical results verify the analysis and reveal clear design tradeoffs: CRN achieves lower OP and higher EC, whereas SRN can be more energy efficient in low-rate or complexity-constrained regimes.
Jiaqing Chen, P. Miao, Chunguo Li et al.· IEEE Transactions on Communi...· 0 citations
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.
Xintong Zhou, Feng Ke, Xiu-Yin Zhang et al.· IEEE Transactions on Communi...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
