流体天线辅助的无蜂窝大规模MIMO非授权随机接入分布式联合解码
Fluid-Antenna-Assisted Distributed Joint Decoding for Cell-Free Massive MIMO Unsourced Random Access
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中文总结 AI 辅助
该研究针对无蜂窝大规模MIMO非授权随机接入的多用户干扰等问题，提出流体天线辅助的分布式联合解码方法，经仿真验证可提升接收功率，实现流体天线与该接入技术的可行集成。
中文摘要 AI 辅助
非授权随机接入（URA）支持大规模 sporadic 连接，但仍受多用户干扰、分布式信道不确定性和局部衰落限制。本文提出一种流体天线辅助的无蜂窝大规模MIMO URA分布式联合解码器：单射频链接入点依次探测相关端口，中央处理器执行感知相关性的期望最大化近似消息传递，为每个接入点选择一个公共有效载荷端口，结合MIMO迭代高斯近似与全分组信道重估及连续干扰消除。固定端口消融实验证实分布式接收、重估和迭代消除的互补优势，流体天线仿真显示其平均及低尾接收功率优于固定端口接收。该框架实现了流体天线与无蜂窝URA的可行集成，并为导频冲突和切换开销下的端到端评估提供了方向。
英文摘要
Unsourced random access (URA) supports massive sporadic connectivity but remains limited by multiuser interference, distributed channel uncertainty, and local fading. This paper proposes a fluid-antenna-assisted distributed joint decoder for cell-free massive MIMO URA. Single-radio-frequencychain access points sequentially sound correlated ports; the central processor then performs correlation-aware expectationmaximization approximate message passing, selects one common payload port per access point, and combines MIMO iterative Gaussian approximation with full-packet channel re-estimation and successive interference cancellation. Fixed-port ablations confirm the complementary benefits of distributed reception, reestimation, and iterative cancellation, while fluid-antenna simulations show improved average and lower-tail received power over fixed-port reception. The framework provides a feasible integration of fluid antennas and cell-free URA and motivates end-to-end evaluation under pilot collisions and switching overhead.
