Compressive sensing based pilot reduction technique for massive MIMO systems

Jun Won Choi, Byonghyo Shim

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

5 Citations (Scopus)

Abstract

Massive multi-input multi-output (MIMO) technique deploys a number of transmit antennas in base-station (BS) to support large number of users and high data throughput. Since BS needs to acquire channel state information from all transmit antennas, substantial amount of downlink pilot signals is required. In this paper, we suggest a new downlink pilot allocation strategy, inspired by the compressed sensing principle, that reduces the density of the pilot significantly. Key observation in the proposed approach is that the sparse structure of the channel impulse response (CIR) tends to change slower than the OFDM symbol rate. Through computer simulations, we show that the proposed scheme outperforms the conventional compressed sensing methods, achieving the performance bound provided by the Oracle-based Kalman smoother.

Original languageEnglish
Title of host publication2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages115-118
Number of pages4
ISBN (Print)9781479971954
DOIs
Publication statusPublished - 2015 Oct 27
Externally publishedYes
EventInformation Theory and Applications Workshop, ITA 2015 - San Diego, United States
Duration: 2015 Feb 12015 Feb 6

Other

OtherInformation Theory and Applications Workshop, ITA 2015
CountryUnited States
CitySan Diego
Period15/2/115/2/6

Keywords

  • OFDM
  • Q measurement
  • Signal to noise ratio

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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  • Cite this

    Choi, J. W., & Shim, B. (2015). Compressive sensing based pilot reduction technique for massive MIMO systems. In 2015 Information Theory and Applications Workshop, ITA 2015 - Conference Proceedings (pp. 115-118). [7308974] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITA.2015.7308974