Massive Unsourced Random Access for Near-Field Communications

• Published in: IEEE transactions on communications Vol. 72; no. 6; pp. 3256 - 3272
• Main Authors: Xie, Xinyu, Wu, Yongpeng, An, Jianping, Ng, Derrick Wing Kwan, Xing, Chengwen, Zhang, Wenjun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Channel estimation; Channels; Clustering; Coherence; Compressed sensing; Dictionaries; Domains; Encoding; Fading channels; Far fields; massive machine-type communications; Massive MIMO; Messages; Near field communication; near-field communications; Random access; Sampling methods; Sparsity; Termination of employment; unsourced random access
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2024.3361529

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 <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-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.