Compressive sampling optimization for user signal parameter estimation in massive MIMO systems

Yujie Gu, Yimin D. Zhang

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

As the most promising technology in wireless communications, massive multiple-input multiple-output (MIMO) faces a significant challenge in practical implementation because of the high complexity and cost involved in deploying a separate front-end circuit for each antenna. In this paper, we apply the compressive sampling technique to reduce the number of required front-end circuits in the analog domain and the computational complexity in the digital domain. Unlike the commonly adopted random projections, we exploit the a priori probability distribution of the user positions to optimize the compressive sampling strategy, so as to maximize the mutual information between the compressed measurements and the direction-of-arrival (DOA) of user signals. With the optimized compressive sampling strategy, we further propose a compressive sampling Capon spatial spectrum estimator for DOA estimation. In addition, the user signal power is estimated by solving a compressed measurement covariance matrix fitting problem. Furthermore, the user signal waveforms are estimated from a robust adaptive beamformer through the reconstruction of an interference-plus-noise compressed covariance matrix. Simulation results clearly demonstrate the performance advantages of the proposed techniques for user signal parameter estimation as compared to existing techniques.

Original languageEnglish
Pages (from-to)105-113
Number of pages9
JournalDigital Signal Processing: A Review Journal
Volume94
DOIs
StatePublished - Nov 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 Elsevier Inc.

Keywords

  • Adaptive beamforming
  • Compressive sampling optimization
  • Massive MIMO
  • Mutual information
  • Parameter estimation

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