2026· Journal of Communications Software and Systems· 0 citations· 37 references
TL;DR
A unified optimization framework that integrates Dinkelbach fractional programming, a rigorously derived weighted minimum mean square error (WMMSE) reformulation, second-order cone programming (SOCP), and Riemannian manifold optimization within a provably convergent alternating structure is developed.
Abstract
—Reconfigurable intelligent surface (RIS)-assisted wireless networks offer significant performance gains; however, energy-efficiency maximization in such systems remains fundamentally challenging due to the coupled active–passive beamforming design and the fractional nature of the objective function. Existing studies typically rely on semidefinite relaxation or optimize surrogate spectral-efficiency metrics, without establishing theoretical equivalence to the original fractional formulation or providing formal convergence guarantees. This paper addresses this gap by formulating joint transmit and RIS phase optimization as a non-convex fractional program with unit-modulus constraints, which is NP-hard. We develop a unified optimization framework that integrates Dinkelbach fractional programming, a rigorously derived weighted minimum mean square error (WMMSE) reformulation, second-order cone programming (SOCP), and Riemannian manifold optimization within a provably convergent alternating structure. Unlike prior approaches, the proposed method preserves stationary-point equivalence to the original fractional objective and is proven to converge to a Karush–Kuhn–Tucker (KKT) point under standard constraint qualification conditions. A detailed per-stage computational complexity analysis is provided, together with empirical runtime evaluation. Simulation results, averaged over 1000 independent Monte Carlo realizations with 95% confidence intervals, demonstrate up to 40.9% improvement in energy efficiency compared with state-of-the-art benchmark schemes under identical system assumptions with 95% confidence interval (CI) [38 . 7% , 43 . 1%] . Sensitivity analysis further confirms robustness with respect to RIS size, user density, transmit power, and phase quantization resolution. These results establish both the theoretical soundness and practical scalability of the proposed framework for energy-efficient RIS-assisted communications.
Validation of the APG algorithm's resilience revealed that it outperformed benchmark algorithms in terms of energy efficiency and execution time, demonstrating its usefulness for challenging optimization tasks, particularly those involving bursty communication.
K. A. Bonsu, Ebenezer Baidoo Baidoo Bediako, K. Darkwah et al.· Applied Mathematics and Stat...· 0 citations
Reconfigurable intelligent surfaces (RISs) are a promising technology for improving the spectral and energy efficiency of future wireless networks, which make use of metasurfaces. However, optimizing RIS configurations typically leads to large-scale, non-convex problems whose complexity grows significantly with the number of scattering elements and the adoption of advanced metasurface architectures. In this paper, we develop a stochastic optimization framework for RIS-aided wireless networks based on continuous versions of the ($a$) cross-entropy (CE) and ($b$) Metropolis-Hastings (MH) methods. Unlike existing stochastic approaches that mainly focus on discrete optimization, the proposed framework directly handles continuous variables and can be readily applied to discrete settings through relaxation and projection. We provide a theoretical characterization of the proposed algorithms, including convergence guarantees and efficiency analysis. The framework is applied to ($i$) achievable-rate maximization with nearly-passive RISs and ($ii$) energy-efficiency maximization with active RISs. Numerical results show that the proposed methods achieve performance comparable to, or better than, state-of-the-art deterministic algorithms, while reducing execution times up to 10 times in representative scenarios.
Davide Gagliardi, Alessio Zappone, Domenico Ciuonzo 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
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 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.
Yongjie Li, Jing Shen, Jizhao Lu et al.· 2026 8th International Confe...· 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
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