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
Xinyu Xie;Yongpeng Wu;Jianping An;Junyuan Gao;Wenjun Zhang;C. Xing;Kai‐Kit Wong;Chengshan Xiao
中科院分区:
计算机科学2区
文献类型:
--
作者:
Xinyu Xie;Yongpeng Wu;Jianping An;Junyuan Gao;Wenjun Zhang;C. Xing;Kai‐Kit Wong;Chengshan Xiao

文献摘要

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本文研究了无源随机接入(URA)方案,以适应众多的机器类型的用户通信到一个基站配备了多个天线。现有的作品采用时隙传输策略,以降低系统的复杂性,他们的框架下工作的耦合压缩感知(CCS)级联的外部树码的内部压缩感知码时隙明智的消息拼接。我们建议,通过利用MIMO信道信息的角度域,冗余所需的CCS树编码器/解码器可以被删除,以提高频谱效率,从而设计一个解耦的传输协议。为了进行活动检测和信道估计,我们提出了一个期望最大化辅助广义近似消息传递算法与马尔可夫随机场的支持结构,它捕获的角度域信道的固有集群稀疏结构。然后,通过基于相似性识别每个活跃用户的时隙分布信道,以聚类解码器的形式执行消息重构。我们提出了时隙平衡的$ K $ -means算法作为聚类解码器的核心,解决了特定应用场景的约束和冲突。大量的仿真结果表明,该方案实现了更好的错误性能在高频谱效率相比,基于CCS的URA计划。
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.