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Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Multi-user Downlink with Reconfigurable Intelligent Metas...
Xuejian Wei, Hui-Ming Wang · 2025-06-23 · via cs.IT updates on arXiv.org

Reconfigurable Intelligent Surfaces (RIS) is a transformative technology with great potential in many applications in wireless communications and realizing the Internet of Everything at sixth generation (6G). In this study, we propose a wireless system where the RIS acts as an antenna, which we call Reconfigurable Intelligent Metasurface Antennas (RIMSA). In particular, the base station (BS) equipped with a RIMSA array performs downlink transmissions to multiple users, where each user has a single or multiple RIMSA/RF links, and we aim to solve the sum-rate maximization problem by jointly optimizing the digital processing matrix of the transceivers and the phase responses of RIMSA array at both BS and users. For the multi-user multiple-input single-output (MU-MISO) scenario, we develop an alternating optimization algorithm to slove the problem, where a fractional programming (FP) is used to optimize the digital processing matrix and a product manifold optimization (PMO) is proposed to provide the optimal phase responses of the RIMSA array at both BS and users. For the multi-user multiple-input multiple-output (MU-MIMO) scenario, we equate it to a weighted sum of mean square errors minimization problem, which can be solved by three subproblems iteratively. Both the optimal digital precoder subproblem and the optimal digital combiner subproblem have closed-form solutions, and the subproblem of RIMSA configuration is solved by the PMO algorithm as well. Simulation results demonstrate that the proposed algorithms achieve significant performance gains over conventional algorithms.