Device Activity Detection and Channel Estimation for Millimeter-Wave Massive MIMO

Yinchuan Li, Yuancheng Zhan, Le Zheng*, Xiaodong Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Millimeter-Wave Massive MIMO is important for beyond 5G or 6G wireless communication networks. The goal of this paper is to establish successful communication between the cellular base stations and devices, focusing on the problem of joint user activity detection and channel estimation. Different from traditional compressed sensing (CS) methods that only use the sparsity of user activities, we develop several Approximate Message Passing (AMP) based CS algorithms by exploiting the sparsity of user activities and mmWave channels. First, a group soft-thresholding AMP is presented to utilize only the user activity sparsity. Second, a hard-thresholding AMP is proposed based on the on-grid CS approach. Third, a super-resolution AMP algorithm is proposed based on atomic norm, in which a greedy method is proposed as a super-resolution denoiser. And we smooth the denoiser based on Monte Carlo sampling to have Lipschitz continuity and present state evolution results. Extensive simulation results show that the proposed method outperforms the previous state-of-the-art methods.

Original languageEnglish
Pages (from-to)1062-1074
Number of pages13
JournalIEEE Transactions on Communications
Volume72
Issue number2
DOIs
Publication statusPublished - 1 Feb 2024

Keywords

  • Compressed sensing
  • approximate message passing
  • atomic norm
  • machine-type communications
  • massive connectivity
  • massive multiple-input multiple-output (MIMO)
  • millimeter-wave
  • state evolution

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