Jul 2026· 2026 8th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT)· pp. 483-486· 0 citations· 12 references
Abstract
Reconfigurable intelligent surface (RIS)-assisted wireless-powered communication networks (WPCNs) introduce a new degree of freedom: the same passive beamforming array can concentrate the downlink energy toward harvesting devices and simultaneously shape the uplink interference environment. In this paper, we study a system where the base station (BS) waveform serves the dual role of wireless energy transfer (WET) and passive target sensing. Using the position error bound (PEB) derived from the equivalent Fisher information matrix (EFIM) as the sensing quality metric, we formulate a joint resource allocation problem that maximizes weighted uplink sum-rate subject to a PEB constraint, energy-causality, block-time sharing, and unit-modulus RIS phase constraints. Focusing on the practically important WET-only sensing case, we show that the problem separates into four tractable subproblems and propose a block coordinate descent (BCD) algorithm: (i) closed-form water-filling for WIT time-power allocation, (ii) semidefinite relaxation (SDR) with Dinkelbach iterations for per-slot WIT-RIS beamforming, (iii) golden-section search for the optimal WET duration, and (iv) a convex SDP for WET-RIS optimization under a PEB constraint. The BCD iterates converge monotonically. Simulations confirm a fundamental rate–sensing tradeoff and demonstrate significant gains from joint RIS-assisted optimization.
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
Fluid antennas (FAs) represent a key enabling technology for the evolution of next-generation wireless networks. In this paper, we propose an FA-empowered integrated sensing and communication (ISAC) system in which a reconfigurable intelligent surface (RIS) is employed to mitigate performance degradation in communication and sensing caused by blockages of line-of-sight links. We consider a joint optimization of transmit beamforming matrices, RIS coefficients, and transmit antenna positions to maximize the sum rate subject to power budget, RIS, and antenna spacing constraints, as well as flexible regions. Due to the non-convexity and complexity of the objective, the alternative algorithm-based framework is used to iteratively solve the original optimization problem, in which the problem is decomposed into several subproblems using the fractional programming algorithm. Specifically, the subproblems regarding precoding, RIS phase shifts, and antenna positions are efficiently solved using semidefinite relaxation, Riemannian steepest-descent, and majorization minimization algorithms, respectively. Simulation results demonstrate the efficiency and superiority of the proposed framework over the conventional RIS-aided fixed-position antenna ISAC system.
Zhijie Lyu, Ye Wang, Rui Wang et al.· IEEE Open Journal of the Com...· 0 citations
We propose a hybrid simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) architecture with dynamically switched active and passive elements to support joint localization, communication, and wireless power transfer (WPT). We first pursue a parallel factor analysis with the alternating least squares (PARAFAC-ALS)-based tensor decomposition approach that decouples the base station (BS)–reconfigurable intelligent surface (RIS) and RIS–user channels, thereby enabling low-overhead channel acquisition. Based on this, we formulate a system energy efficiency (EE) maximization problem, subject to the spectral efficiency (SE) requirements of communication users, sensing signal-to-interference-plus-noise ratio constraints, and the nonlinear energy harvesting requirements of energy-harvesting users. The optimization problem is nonconvex since the transmit power allocation, STAR-RIS coefficients, and active/passive mode assignments are tightly coupled in both the objective and constraints. We address this issue by alternating between two subproblems, and solving them via fractional programming, successive convex approximation and a multi-seed greedy strategy employed as an initialization step. Numerical results demonstrate that selectively activating a small, well-chosen subset of STAR-RIS elements achieves $1.5\times $ to $3\times $ EE improvements compared with fully passive/active architectures, while satisfying communication, sensing, and power-transfer requirements.
Haoran Ni, M. Mohammadi, Xidong Mu 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, Tianyu Fang, Marco Di Renzo et al.· 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, Xiuyin Zhang et al.· IEEE Transactions on Communi...· 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
