大规模MIMO接收机的无源随机访问:利用角域稀疏性

Xinyu Xie, Yongpeng Wu
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引用次数: 1

摘要

本文研究了无源随机接入(URA)方案,以适应大量机器型用户与大规模MIMO基站的通信。现有工作采用开槽传输策略来降低系统复杂性,并在耦合压缩感知(CCS)框架下运行,将外部树码与内部压缩感知码串联起来进行消息拼接。我们观察到稀疏角域MIMO信道可以帮助解耦CCS方案,并引入不需要树编码器/解码器的解耦槽传输方案。我们提出了一种新的MRF-GAMP方法,用于捕获角域信道的结构化稀疏性,用于活动检测和信道估计。然后,通过聚类算法将强相关的时隙通道重新排列成组,从而实现消息重构。大量的仿真表明,与CCS方案相比,我们的方法具有更好的误差性能和更高的频谱效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unsourced Random Access with a Massive MIMO Receiver: Exploiting Angular Domain Sparsity
This paper investigates the unsourced random access (URA) scheme to accommodate a large amount of machine-type users communicating to a massive MIMO base station. Existing works adopt a slotted transmission strategy to reduce system complexity and operate under the framework of coupled compressed sensing (CCS), concatenating an outer tree code to an inner compressed sensing code for message stitching. We observe that the sparse angular domain MIMO channel can help decouple the CCS scheme and introduce an uncoupled slotted transmission scheme without the tree encoder/decoder. We propose a novel MRF-GAMP method capturing the structured sparsity of the angular domain channel for activity detection and channel estimation. Then, message reconstruction is based on rearranging strongly correlated slot-wise channels into groups by a clustering algorithm. Extensive simulation shows that our approach achieves a better error performance and a higher spectral efficiency compared to the CCS scheme.
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