Distributed Antenna Array and RIS-Assisted Planning Framework for Intelligent Coverage Optimization in B5G/6G Cell-Free Massive MIMO

Reconfigurable Intelligent Surface-Assisted 6G Communication: A Beamforming and Channel Modeling Perspective

A novel deep reinforcement learning-based cell selection algorithm for efficient mobility management in 6G networks

Simulation results position D3QN-PER as a strong candidate for deployment as a near-RT RIC xApp within the O-RAN architecture, advancing the vision of AI-native mobility management for 6G.

Capacity Maximization for 6DMA- and STAR-RIS-Enhanced Wireless Networks via Spatial Multiplexing and Directional Modulation

Simulation results show that the proposed method can significantly improve the sum rate of users as compared to benchmark with FPA + Optimized STAR-RIS, 6DMA + Random STAR-RIS, and FPA + Random STAR-RIS.

Multi-RIS-Assisted Satellite Compact Ultra-Massive Antenna Array for Massive Uplink Transmission

High-capacity satellite network is the cornerstone of future space-air-ground integrated networks. However, the satellite uplink transmissions still face critical challenges, including severe path loss, complex multi-user interference, and payload constraints. Recently, Reconfigurable Intelligent Surfaces (RIS) and Fluid Antenna Systems (FAS) have shown promise for satellite communications through their dynamic signal reconfiguration. This paper proposes a multi-RIS-assisted satellite Compact Ultra-Massive Antenna Array (CUMA) architecture for multi-user satellite uplink transmission. Specifically, we deploy multiple RISs on the terrestrial side to separate interfering Line-of-Sight (LoS) channels via optimized phase shifts, and adopt a CUMA receiver on the satellite to further mitigate interference through FAS port selection. To solve a sum-rate maximization problem, we alternately optimize FAS port selection using a Forward-Backward Greedy Selection (FBGS) algorithm and RIS phase shifts based on Fractional Programming (FP). To the best of our knowledge, this is the first work to jointly optimize multi-RIS and CUMA in a satellite uplink context, where strong LoS and extreme path loss fundamentally distinguish the design from terrestrial counterparts. Simulation results confirm the effectiveness of the proposed architecture across frequency bands. At 6 GHz, our scheme achieves 181% and 32% rate gains over fixed antennas and traditional CUMA schemes, respectively, while the gains also reach 138% and 27% at 26 GHz, illustrating superiority in both interference-limited and noise-limited regimes.

STAR-RIS-Aided Wideband Cell-Free ISAC: Joint Resource Allocation and Beamforming

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.

Deep Reinforcement Learning–Enhanced Non-Orthogonal Multiple Access Framework for UAV-Assisted Terahertz 6G Communication Networks: A Large-Scale Monte Carlo Simulation Study

This paper presents a Deep Reinforcement Learning (DRL)-enhanced Non-Orthogonal Multiple Access (NOMA) framework for UAV-assisted Terahertz (THz) 6G communication networks. The proposed framework introduces three key novelties: (i) a K-means clustering algorithm for 300-node UAV swarm coordination that reduces inter-cluster interference through intelligent cluster-head selection; (ii) an adaptive NOMA user-pairing strategy based on real-time channel-quality estimation that dynamically assigns weak and strong users to optimal power-allocation levels; and (iii) a Q-learning-based distributed power-control mechanism that continuously optimizes UAV transmission power in response to time-varying THz channel conditions and network load. A large-scale Monte Carlo simulation (M = 10 runs, 200 rounds, 3000 × 3000 m² deployment area) validates the proposed scheme against traditional NOMA and Orthogonal Multiple Access (OMA) benchmarks. Results demonstrate that the proposed DRL-NOMA achieves an average system throughput of 185.4 Mbps — a 29.5 % improvement over traditional NOMA and a 124.2 % gain over OMA — while maintaining a Packet Delivery Ratio (PDR) of 0.8812 and a Jain’s Fairness Index of 0.9321. Energy-efficiency and outage-probability analyses further confirm the superiority and practical deployability of the proposed scheme for next-generation heterogeneous 6G networks.