Adaptive Bayesian Channel Estimation for Millimeter-Wave MIMO Systems with Hybrid Architecture

Rongrong Qian, Mathini Sellathurai, Pat Chambers, Tharmalingam Ratnarajah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The hybrid multiple-input-multiple-output (MIMO) transceiver architecture is a promising solution to reducing hardware and power cost at millimeter-wave (mmWave) frequencies. However, channel estimation is a major issue for the deployment such system models. Thus, this work shows that by exploiting the sparse scattering nature of the mmWave channel, an efficent channel estimation algorithm can be achieved by utilizing state-of-the-art compressive sensing (CS) techniques. In general previous CS-based channel estimation methods consider an on-grid sparse signal representation problem, however this is not truly realistic to the scenario of mmWave massive MIMO systems. To achieve a more realistic channel estimation algorithm for the mmWave MIMO system, this work considers an off-grid signal model approach, i.e., the directions of sparse channels are not confined on the angular grid for sparse signal formulation. A new adaptive channel estimation method is proposed by using Bayesian CS (BCS) to accurately and efficiently sense channels in terms of an off-grid signal model. A measurement of recovery uncertainty output by BCS is exploited to adaptively design the sensing matrix, thereby improving its estimation performance.

Original languageEnglish
Title of host publication2018 52nd Asilomar Conference on Signals, Systems, and Computers
PublisherIEEE
Pages274-278
Number of pages5
ISBN (Electronic)9781538692189
DOIs
Publication statusPublished - 19 Feb 2019
Event52nd Asilomar Conference on Signals, Systems and Computers 2018 - Pacific Grove, United States
Duration: 28 Oct 201831 Oct 2018

Publication series

NameAsilomar Conference on Signals, Systems, and Computers
PublisherIEEE
ISSN (Electronic)2576-2303

Conference

Conference52nd Asilomar Conference on Signals, Systems and Computers 2018
Abbreviated titleACSSC 2018
CountryUnited States
CityPacific Grove
Period28/10/1831/10/18

Keywords

  • adaptive channel estimation
  • Bayesian compressive sensing
  • Massive MIMO
  • millimeter wave

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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  • Cite this

    Qian, R., Sellathurai, M., Chambers, P., & Ratnarajah, T. (2019). Adaptive Bayesian Channel Estimation for Millimeter-Wave MIMO Systems with Hybrid Architecture. In 2018 52nd Asilomar Conference on Signals, Systems, and Computers (pp. 274-278). (Asilomar Conference on Signals, Systems, and Computers). IEEE. https://doi.org/10.1109/ACSSC.2018.8645273