Variable earns profit: Improved adaptive channel estimation using sparse VSS-NLMS algorithms

Guan Gui, Linglong Dai, Shinya Kumagai, Fumiyuki Adachi

研究成果: Conference contribution

21 被引用数 (Scopus)

抄録

Accurate channel estimation is essential for broadband wireless communications. Adaptive sparse channel estimation schemes based on normalized least mean square (NLMS) have been proposed to exploit channel sparsity for improved performance. However, their performance bound as derived in this paper indicates that the invariable step size (ISS) usually used for iteration in these schemes would lead to performance loss or/and slow convergence speed as well as high computational cost. To solve this problem, based on the observation that a large step size is preferred for fast convergence while a small step size is preferred for accurate estimation, we then propose to replace the ISS by the variable step size (VSS) to improve the performance of sparse channel estimation. The key idea is that the VSS can be adaptive to the estimation error in each iteration, i.e., a large step size is used in the case of large estimation error to accelerate the convergence speed, while a small step size is used when the estimation error is small to improve the steady-state estimation accuracy. Finally, simulation results verify that better mean square error (MSE) and bit error rate (BER) performance could be achieved by the proposed scheme.

本文言語English
ホスト出版物のタイトル2014 IEEE International Conference on Communications, ICC 2014
出版社IEEE Computer Society
ページ4390-4394
ページ数5
ISBN(印刷版)9781479920037
DOI
出版ステータスPublished - 2014 1 1
イベント2014 1st IEEE International Conference on Communications, ICC 2014 - Sydney, NSW, Australia
継続期間: 2014 6 102014 6 14

出版物シリーズ

名前2014 IEEE International Conference on Communications, ICC 2014

Other

Other2014 1st IEEE International Conference on Communications, ICC 2014
CountryAustralia
CitySydney, NSW
Period14/6/1014/6/14

ASJC Scopus subject areas

  • Computer Networks and Communications

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