Adaptive sparse channel estimation for time-variant MIMO communication systems

Guan Gui, Abolfazl Mehbodniya, Fumiyuki Adachi

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

4 Citations (Scopus)

Abstract

Channel estimation problem is one of the key technical issues in time-variant multiple-input multiple-output (MIMO) communication systems. To estimate the MIMO channel, least mean square (LMS) algorithm was applied to adaptive channel estimation (ACE). Since the MIMO channel is often described by sparse channel model, such sparsity can be exploited to improve the estimation performance by adaptive sparse channel estimation (ASCE) methods using sparse LMS algorithms. However, conventional ASCE methods have two main drawbacks: 1) sensitive to random scale of training signal and 2) unstable in low signal-to-noise ratio (SNR) regime. To overcome the two harmful factors, in this paper, we propose a novel ASCE method using normalized LMS (NLMS) algorithm (ASCE-NLMS). In addition, we also proposed an improved ASCE method using normalized least mean fourth (NLMF) algorithm (ASCE-NLMF). Two proposed methods can exploit the channel sparsity effectively. Also, stability of the proposed methods is confirmed by mathematical derivation. Computer simulation results show that the proposed sparse channel estimation methods can achieve better estimation performance than conventional methods.

Original languageEnglish
Title of host publication2013 IEEE 78th Vehicular Technology Conference, VTC Fall 2013
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 IEEE 78th Vehicular Technology Conference, VTC Fall 2013 - Las Vegas, NV, United States
Duration: 2013 Sep 22013 Sep 5

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252

Other

Other2013 IEEE 78th Vehicular Technology Conference, VTC Fall 2013
CountryUnited States
CityLas Vegas, NV
Period13/9/213/9/5

Keywords

  • Adaptive sparse channel estimation (ASCE)
  • Least mean fourth (LMF)
  • Least mean square (LMS)
  • Multiple-input multiple-output (MIMO)
  • Normalized LMF (NLMF)

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Adaptive sparse channel estimation for time-variant MIMO communication systems'. Together they form a unique fingerprint.

  • Cite this

    Gui, G., Mehbodniya, A., & Adachi, F. (2013). Adaptive sparse channel estimation for time-variant MIMO communication systems. In 2013 IEEE 78th Vehicular Technology Conference, VTC Fall 2013 [6692085] (IEEE Vehicular Technology Conference). https://doi.org/10.1109/VTCFall.2013.6692085