Massive Unsourced Random Access: Exploiting Angular Domain Sparsity
Massive Unsourced Random Access: Exploiting Angular Domain Sparsity
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DOI:
10.1109/tcomm.2022.3153957
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发表时间:
2022-02
影响因子:
8.3
通讯作者:
Xinyu Xie;Yongpeng Wu;Jianping An;Junyuan Gao;Wenjun Zhang;C. Xing;Kai‐Kit Wong;Chengshan Xiao
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文献类型:
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作者:
Xinyu Xie;Yongpeng Wu;Jianping An;Junyuan Gao;Wenjun Zhang;C. Xing;Kai‐Kit Wong;Chengshan Xiao
This paper investigates the unsourced random access (URA) scheme to accommodate numerous machine-type users communicating to a base station equipped with multiple antennas. Existing works adopt a slotted transmission strategy to reduce system complexity; they operate under the framework of coupled compressed sensing (CCS) which concatenates an outer tree code to an inner compressed sensing code for slot-wise message stitching. We suggest that by exploiting the MIMO channel information in the angular domain, redundancies required by the tree encoder/decoder in CCS can be removed to improve spectral efficiency, thereby an uncoupled transmission protocol is devised. To perform activity detection and channel estimation, we propose an expectation-maximization-aided generalized approximate message passing algorithm with a Markov random field support structure, which captures the inherent clustered sparsity structure of the angular domain channel. Then, message reconstruction in the form of a clustering decoder is performed by recognizing slot-distributed channels of each active user based on similarity. We put forward the slot-balanced $ K $ -means algorithm as the kernel of the clustering decoder, resolving constraints and collisions specific to the application scene. Extensive simulations reveal that the proposed scheme achieves a better error performance at high spectral efficiency compared to the CCS-based URA schemes.