Author

Qiong Wu

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Preprint Jul 2026

Transmit Beamforming Design for Integrated Sensing and Communication Using Transmissive RIS Transceiver

Integrated sensing and communication (ISAC) is a key technology for future wireless networks, calling for hardware-efficient architectures to jointly support communication and sensing. In this paper, a transmissive reconfigurable intelligent surface (TRIS) transceiver is leveraged to enable an ISAC system. Under the considered system model, we investigate transmit beamforming design for the TRIS transceiver to maximize the sum-rate/beampattern gain, subject to the predefined sensing beampattern gain/communication rate thresholds and the per-unit power constraints of the TRIS transceiver. Since the objective functions and constraints are non-convex, the above two optimization problems are highly challenging. To resolve the difficult optimization problems, we combine the fractional programming (FP) method and the majorization-minimization (MM) framework to develop second-order cone programming (SOCP)-based solutions. Since the per-element power constraints introduce a large number of constraints, this increases the complexity of solving the optimization problems. By splitting the coupling constraints and applying the alternating direction method of multipliers (ADMM) framework, we propose two analytic-based algorithms for efficiently updating the beamformer configurations in the sum-rate and beampattern gain maximization problems, respectively. Simulation results demonstrate the convergence and effectiveness of the proposed algorithms, and show that the low-complexity algorithms achieve performance close to the SOCP-based benchmarks with substantially reduced computational complexity.

Yuan Guo, Wen Chen, Yang Liu et al. · 0 citations

Jul 2026

Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learning

Federated learning (FL) over wireless networks suffers from significant training latency and degraded convergence due to unreliable wireless transmission, especially under blocked propagation environments. Although reconfigurable intelligent surfaces (RISs) can improve communication reliability, existing wireless FL studies rarely characterize the trade-off between learning convergence and communication delay under modulation-dependent transmission errors. In this paper, we consider a wireless FL system operating under RIS-assisted blocked-link propagation scenarios, and focus on adaptive modulation and sub-channel allocation for convergence-latency aware communication design. By characterizing the effect of symbol errors on uploaded local gradients, we derive a convergence-related upper bound that reveals the impact of symbol error rate (SER) on FL loss decay. Based on this result, we formulate a joint convergence-latency optimization problem, which is cast as a mixed-integer nonlinear programming (MINLP) problem, and solve it using a low-complexity hybrid alternating optimization framework. Extensive experiments on MNIST, CIFAR-10, and Speech Commands show that the proposed scheme consistently achieves faster convergence and higher test accuracy than existing adaptive communication schemes, especially in complex tasks and challenging wireless scenarios.

Liwei Wang, Wen Chen, Jun Li et al. · arXiv.org · 0 citations