Weight Adjustment in Channel Estimation using Gibbs Sampling for MIMO Systems

Kenshiro Chuman, Yukitoshi Sanada

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

Abstract

Channel estimation is required to demodulate the multiple-input multiple-output (MIMO) signals and there are blind and non-blind channel estimation schemes. One of non-blind channel estimation schemes is using Gibbs sampling. In channel estimation using Gibbs sampling, when the number of received data symbols is small, estimation accuracy may be deteriorated owing to the unreliability of weights. In order to solve this problem, this paper introduces a coefficient for adjusting the weights used in channel estimation with Gibbs sampling. When the number of data symbols is small, larger coefficient values are found to be more suitable because they suppress the fluctuation of channel estimation. Numerical results obtained though computer simulation show that the proposed scheme improves by about 5dB as compared with a decision directed scheme in a 4 × 4 MIMO system when the number of data symbols is one.

Original languageEnglish
Title of host publication2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665413688
DOIs
Publication statusPublished - 2021
Event94th IEEE Vehicular Technology Conference, VTC 2021-Fall - Virtual, Online, United States
Duration: 2021 Sep 272021 Sep 30

Publication series

NameIEEE Vehicular Technology Conference
Volume2021-September
ISSN (Print)1550-2252

Conference

Conference94th IEEE Vehicular Technology Conference, VTC 2021-Fall
Country/TerritoryUnited States
CityVirtual, Online
Period21/9/2721/9/30

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Weight Adjustment in Channel Estimation using Gibbs Sampling for MIMO Systems'. Together they form a unique fingerprint.

Cite this