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.
Kai Feng, Runke Fan, Tianheng Xu et al.· IEEE Open Journal of the Com...· 0 citations
The unprecedented growth of machine-type devices has underscored the need for fundamental solutions to support emerging massive connectivity. In particular, unsourced random access (URA) has emerged as a promising paradigm, reframing the massive connectivity problem as a coding-theoretic challenge with favorable energy and spectral efficiency. Among the widely studied URA models, the Gaussian multiple-access channel (GMAC) and multi-input multi-output (MIMO) systems are of particular significance. Sparse code design is well-suited for URA, offering scalable solutions while retaining many advantages of legacy access protocols. However, existing sparse code designs often suffer from limited sparsity control, inefficient interference cancellation, and a strong dependence on specific channel code designs, posing challenges for long-term adaptability as more powerful channel codes continue to evolve. In MIMO-URA systems, additional activity detection and channel estimation phases typically lead to increased missed detection (MD) and false alarm (FA) errors compared with the GMAC model, which does not require these phases. While prior studies have predominantly focused on minimizing MD errors, the effective mitigation of FA errors remains an open problem. To address this challenge, we propose a sparse code with slotted transmission under the GMAC model, combined with an analytical power division strategy to enhance interference cancellation. Furthermore, we introduce a novel MIMO receiver framework based on joint pattern–data–channel (JPDC) estimation, which significantly reduces FA errors by leveraging the intrinsic correlation between user activity and transmitted data. Notably, the proposed method achieves improved overall system performance without requiring additional transmission overhead or complex algorithms.
Zhentian Zhang, M. J. Ahmadi, kai-kit Wong et al.· IEEE Transactions on Wireles...· 5 citations