Adaptive sparse channel estimation for time-variant MIMO communication systems

Guan Gui, Abolfazl Mehbodniya, Fumiyuki Adachi

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2013 IEEE 78th Vehicular Technology Conference, VTC Fall 2013
DOI
出版ステータスPublished - 2013 12 1
イベント2013 IEEE 78th Vehicular Technology Conference, VTC Fall 2013 - Las Vegas, NV, United States
継続期間: 2013 9 22013 9 5

出版物シリーズ

名前IEEE Vehicular Technology Conference
ISSN(印刷版)1550-2252

Other

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

ASJC Scopus subject areas

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

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