Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2014, Vol. 37 ›› Issue (3): 1-6.doi: 10.13190/j.jbupt.2014.03.001

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Joint Sparse Model Based OFDM Compressed Sensing Channel Estimation

GUO Wen-bin, LI Chun-bo, LEI Di, WANG Wen-bo   

  1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2013-01-01 Online:2014-06-28 Published:2014-06-08

Abstract:

Pilot design schemes and their corresponding channel estimation methods for orthogonal frequency division multiplexing(OFDM)system based on compressed sensing theory are studied. With the channel sparse impulse response and slow varying character, a joint sparse model (JSM) for the channel estimation in OFDM system is proposed. With such joint sparse model, the channel estimations of continuous OFDM symbol periods are converted into a sparse vectors recovery problem with joint sparse model, which improves the estimation performance. Different channel estimation methods for shortwave OFDM system are compared. Simulation shows that the proposed compressed sensing based channel estimation scheme brings out better performance compared with conventional least square channel estimation method and symbol-by-symbol compressed sensing channel estimation method. Simulation also shows that the proposed method has better estimation performance under time-variant channel.

Key words: channel estimation, orthogonal frequency division multiplexing, compressed sensing, joint sparse model

CLC Number: