Massive Unsourced Random Access for Near-Field Communications
IEEE Transactions on Communications(2024)
摘要
This paper investigates the unsourced random access (URA) problem with a
massive multiple-input multiple-output receiver that serves wireless devices in
the near-field of radiation. We employ an uncoupled transmission protocol
without appending redundancies to the slot-wise encoded messages. To exploit
the channel sparsity for block length reduction while facing the collapsed
sparse structure in the angular domain of near-field channels, we propose a
sparse channel sampling method that divides the angle-distance (polar) domain
based on the maximum permissible coherence. Decoding starts with retrieving
active codewords and channels from each slot. We address the issue by
leveraging the structured channel sparsity in the spatial and polar domains and
propose a novel turbo-based recovery algorithm. Furthermore, we investigate an
off-grid compressed sensing method to refine discretely estimated channel
parameters over the continuum that improves the detection performance.
Afterward, without the assistance of redundancies, we recouple the separated
messages according to the similarity of the users' channel information and
propose a modified K-medoids method to handle the constraints and collisions
involved in channel clustering. Simulations reveal that via exploiting the
channel sparsity, the proposed URA scheme achieves high spectral efficiency and
surpasses existing multi-slot-based schemes. Moreover, with more measurements
provided by the overcomplete channel sampling, the near-field-suited scheme
outperforms its counterpart of the far-field.
更多查看译文
关键词
Compressed sensing,massive machine-type communications,massive MIMO,near-field communications,unsourced random access
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要