Sampling-based tracking of time-varying channels for millimeter wave-band communications

Jin Hyeok Yoo, Jisu Bae, Sun Hong Lim, Sunwoo Kim, Jun Won Choi, Byonghyo Shim

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

10 Citations (Scopus)

Abstract

In this paper, we propose a new recursive sparse channel recovery algorithm which can track time-varying support of angular domain channel response vector in mobility scenario for millimeter wave-band communications. We model the angle of departure (AoD) and the angle of arrival (AoA) using discrete state Markov random process and derive joint estimation of the time-varying support and amplitude of the angular domain channel vector. Using sequential Monte Carlo (SMC) method, the proposed channel estimation scheme tracks the support by drawing the samples from a posteriori distribution of the support indices while capturing the dynamics of time-varying amplitude using Kalman filter. Our simulation results show that the proposed algorithm yields significantly better tracking performance than the existing compressed sensing schemes.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Communications, ICC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467389990
DOIs
Publication statusPublished - 2017 Jul 28
Externally publishedYes
Event2017 IEEE International Conference on Communications, ICC 2017 - Paris, France
Duration: 2017 May 212017 May 25

Other

Other2017 IEEE International Conference on Communications, ICC 2017
CountryFrance
CityParis
Period17/5/2117/5/25

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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    Yoo, J. H., Bae, J., Lim, S. H., Kim, S., Choi, J. W., & Shim, B. (2017). Sampling-based tracking of time-varying channels for millimeter wave-band communications. In 2017 IEEE International Conference on Communications, ICC 2017 [7996518] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICC.2017.7996518