Massive Unsourced Random Access: Exploiting Angular Domain Sparsity

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 c...

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Vydáno v:IEEE transactions on communications Ročník 70; číslo 4; s. 2480 - 2498
Hlavní autoři: Xie, Xinyu, Wu, Yongpeng, An, Jianping, Gao, Junyuan, Zhang, Wenjun, Xing, Chengwen, Wong, Kai-Kit, Xiao, Chengshan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Activity detection
Algorithms
Antenna arrays
Channel estimation
Clustering
Clustering algorithms
Coders
Communication
Compressed sensing
Decoding
Domains
Encoders-Decoders
Encoding
Fields (mathematics)
massive machine-type communications
Message passing
Random access
Redundancy
Sparsity
Stitching
ISSN:0090-6778, 1558-0857
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Shrnutí: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 <inline-formula> <tex-math notation="LaTeX"> K </tex-math></inline-formula>-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.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2022.3153957