PASTd-based CSI Tracking in Massive MIMO Systems

Wen Kuang Yang, Chia Min Shen, Tzu-Hsien Sang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Most conventional semi-blind channel estimation schemes for MU-MIMO systems are based on eigenval decomposition (EVD) or singular value decomposition (SVD). However, EVD-or SVD-based channel estimation would impose a high computational complexity when the base station is equipped with large number antennas. Those methods are not well suited for real-time processing, especially for Channel State Information (CSI) tracking in time-varying environments. In order to reduce the computational complexity, a PASTd-based (Projection Approximation Subspace Tracking with deflation) CSI tracking algorithm is proposed. The PASTd-based algorithm converges fast, has low computational complexity, and can operate with very small training overhead. Simulation results show that the proposed algorithm can effectively track the CSI with mild Doppler spreads. Only one pilot symbol per user at the beginning of transmission session is needed to resolve the multiplicative factor ambiguity.

Original languageEnglish
Title of host publication2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538668115
DOIs
StatePublished - 31 Jan 2019
Event23rd IEEE International Conference on Digital Signal Processing, DSP 2018 - Shanghai, China
Duration: 19 Nov 201821 Nov 2018

Publication series

NameInternational Conference on Digital Signal Processing, DSP
Volume2018-November

Conference

Conference23rd IEEE International Conference on Digital Signal Processing, DSP 2018
CountryChina
CityShanghai
Period19/11/1821/11/18

Keywords

  • Massive MIMO
  • PASTd
  • channel estimation
  • channel tracking
  • subspace tracking

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