joint active-passive beamforming

This work addresses the high computational complexity and performance trade-off in joint active/passive beamforming optimization for BD-RIS-aided multi-user MIMO systems under symmetric unitary constraints. To tackle this, we propose an alternating iterative closed-form algorithm that integrates matrix decomposition, the weighted minimum mean square error (WMMSE) framework, and gradient projection. Furthermore, we introduce a beyond-diagonal structural model for the BD-RIS and a joint channel reconstruction technique, thereby overcoming fundamental limitations of conventional passive beamforming. The proposed method achieves near-optimal weighted sum-rate maximization with low computational overhead: it reduces complexity by over 60% compared to state-of-the-art approaches, significantly outperforms heuristic and semidefinite relaxation-based benchmarks in rate performance, and closely approaches the optimal solution.

This paper addresses the non-convex mixed-integer nonlinear programming (MINLP) problem of jointly optimizing user scheduling, target association, and beamforming in integrated sensing and communication (ISAC) systems. To tackle this challenge, we propose a globally optimal joint design framework. Our key contributions are threefold: (i) we formulate an exact mixed-integer linear programming (MILP) reformulation of the original problem, enabling globally optimal solutions; (ii) we adopt low-resolution, constant-modulus, finite-phase-shift beamforming to ensure hardware feasibility without compromising performance; and (iii) we replace conventional sequential heuristic approaches with end-to-end joint optimization of sensing and communication resources. Simulation results demonstrate that the proposed method significantly outperforms staged designs in localization accuracy, communication rate, and robustness—validating the fundamental advantages of joint optimization for multi-objective trade-offs and cross-scenario generalization.

To address critical bottlenecks in programmable metasurface (PM) transmitters—including limited modulation order, symbol-level spatial inconsistency, and severe harmonic interference—this work proposes the first reflective heterodyne-based mixer array architecture. Leveraging co-designed digital upconversion and amplitude-phase-decoupled reconfigurable reflective mixing, the architecture decouples baseband signal generation from RF beamforming. It enables arbitrary-order QAM modulation, multi-stream interference suppression, and spatial diversity while completely eliminating harmonic distortion. Experimental validation on a 5.8 GHz prototype demonstrates a linear-region throughput of 20 Mbps, isotropic constellation generation, and time-frequency coherent applications such as Doppler spoofing—thereby surpassing the fundamental performance limits of conventional switch-based PM transmitters.

To address severe inter-cell interference—particularly degrading edge-user performance—in ultra-dense multi-cell 6G networks caused by aggressive spectrum reuse, this paper proposes a joint beamforming and reflection coefficient optimization framework leveraging beyond-diagonal reconfigurable intelligent surfaces (BD-RIS). Departing from conventional diagonal RISs constrained to phase-only modulation, BD-RIS enables more flexible electromagnetic field coordination via manifold optimization under unit-modulus constraints. The non-convex joint optimization problem is efficiently tackled by integrating the weighted minimum mean square error (WMMSE) reformulation, Lagrangian duality theory, and an alternating optimization algorithm. Simulation results demonstrate that the proposed scheme significantly improves the weighted sum rate in multi-user MIMO downlink transmission, outperforming all baseline methods. This validates BD-RIS’s effectiveness and superiority in mitigating both intra-cell and inter-cell interference while enhancing edge-user communication reliability and throughput.

Conventional single-layer reconfigurable intelligent surfaces (RIS) in multi-user MISO downlink systems suffer from limited wave-domain processing capability and rely heavily on digital beamforming and high-resolution DACs. Method: This paper proposes a stacked intelligent metasurface (SIM) architecture, deploying multiple cascaded reconfigurable metasurface layers at the base station to enable fully analog-domain beamforming. We formulate a novel wave-domain joint optimization model that co-designs transmit power allocation and discrete phase shifts—first of its kind—to maximize the system’s sum rate. An efficient algorithm is developed based on alternating optimization and electromagnetic scattering modeling. Contribution/Results: Under identical antenna count and power constraints, the proposed SIM architecture achieves approximately 200% higher sum rate than conventional MISO systems, while exhibiting significantly lower computational complexity compared to benchmark schemes.

This study addresses the challenging mixed finite- and infinite-dimensional non-convex optimization problem in continuously transmitting reconfigurable intelligent surface (RIS)-aided multi-user downlink systems. Based on a spherical wave channel model and finite-path field response vectors, this work jointly optimizes base station precoding and the continuous aperture phase profile to maximize the weighted sum rate. An alternating optimization framework, integrating the calculus of variations with the minorization-maximization technique, is developed to derive an exact closed-form solution for the infinite-dimensional phase function. Simulation results demonstrate that the proposed scheme significantly outperforms benchmark methods, fully validating the system gains achieved through spatially continuous phase control and a large effective receiving area.

To address the beamforming limitation in integrated sensing and communication (ISAC) systems with reconfigurable intelligent surfaces (RISs) arising from unknown direction-of-arrival (DoA) priors, this paper proposes a wide-beam array pattern synthesis method tailored for discrete-phase RISs. To overcome the non-convex optimization challenges imposed by discrete-phase constraints and unit-modulus requirements, we innovatively integrate a penalty function method with convex relaxation—relaxing the discrete-phase constraint to the convex hull boundary—and solve the resulting problem efficiently within the Minorization-Maximization (MM) framework, enabling joint amplitude and phase control. The proposed method significantly broadens the received power coverage region and enhances robustness of angle-of-arrival (AoA) estimation: experiments demonstrate a 1–2 order-of-magnitude reduction in AoA estimation mean-square error at medium-to-low SNRs, and maintain high-probability target detection even when SNR degrades by 8 dB.

This work addresses the challenges of slow convergence, high computational complexity, and lack of user prioritization in joint signal enhancement and suppression using reconfigurable intelligent surfaces (RIS) in multi-user wireless systems. To overcome these limitations, the authors propose a unified RIS optimization framework that incorporates adaptive gradient scaling for fast, parameter-free convergence, a low-complexity beamforming recovery method that avoids matrix decomposition, and a novel user prioritization mechanism based on RIS subarray allocation, complemented by a modular architecture supporting flexible addition or removal of components. Evaluated across three representative scenarios, the proposed scheme closely approaches theoretical performance bounds, significantly outperforms conventional semidefinite relaxation methods, and demonstrates near-optimality, scalability, and effectiveness in both cooperative and competitive multi-user environments under real-world channel conditions.

This work addresses the challenge of jointly optimizing energy efficiency for both communication and sensing performance in extremely large-scale antenna arrays. To this end, the paper proposes a novel triple-hybrid beamforming architecture tailored for integrated sensing and communication (ISAC) systems and, for the first time, applies it to programmable metasurface antenna scenarios. A multi-objective optimization framework is formulated to simultaneously maximize communication signal-to-noise ratio and sensing power toward target directions, subject to total power consumption and physical constraints. A closed-form iterative algorithm is devised to reduce computational complexity. Simulation results demonstrate that the proposed approach achieves significant improvements in spatial gain and energy efficiency compared to conventional hybrid beamforming schemes, with only a minor degradation in beam alignment accuracy.

This work addresses the bottlenecks in spectral efficiency and onboard hardware complexity faced by low Earth orbit (LEO) satellite constellations by introducing metasurface antennas into LEO satellite communications for the first time. The authors propose a mixed-integer nonlinear optimization framework that jointly optimizes user scheduling and passive beamforming. Leveraging an alternating optimization strategy, the approach employs minimum-cost maximum-flow (MCMF) to achieve polynomial-time-complexity user scheduling and integrates weighted minimum mean square error (WMMSE) with semidefinite relaxation (SDR) to design high-precision beamforming patterns that effectively suppress multiuser interference. Simulation results demonstrate that the proposed method significantly enhances both the system’s weighted sum rate and resource utilization efficiency.