Kalman Filter Based Recursive Estimation of Slowly Fading Sparse Channel in Impulsive Noise Environment for OFDM Systems

被引：18
|
作者：
Lv, Xinrong [1 ,2 ]
Li, Youming [1 ]
Wu, Yongqing [3 ,4 ]
Liang, Hui [5 ]
机构：
[1] Ningbo Univ, Fac Informat Sci & Engn, Ningbo 315211, Peoples R China
[2] Zhejiang Business Technol Inst, Coll Elect Informat, Ningbo 315211, Peoples R China
[3] Chinese Acad Sci, Inst Acoust, Beijing 200032, Peoples R China
[4] Peng Cheng Lab, Shenzhen 518066, Peoples R China
[5] Ningbo Sanxing Elect Co Ltd, Ningbo 315211, Peoples R China
来源：
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | 2020年 / 69卷 / 03期
基金：
中国国家自然科学基金;
关键词：
OFDM; sparse Bayesian learning (SBL); Kalman filtering and smoothing; channel estimation; impulsive noise; WIRELESS;
D O I：
10.1109/TVT.2020.2965005
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we propose a recursive sparse channel estimation algorithm in the presence of impulse noise. Firstly the channel impulse response and impulsive noise are jointly viewed as an unknown sparse vector. Then a novel recursive Kalman filtering based compressed sensing algorithm for joint channel and impulsive noise estimation is proposed by using the first order autoregressive model for tracking slowly time varying wireless channel. This algorithm can be extended also to quasi-static, block-fading scenario conveniently. Simulation results illustrate the efficiency of the proposed techniques in terms of the mean square error and bit error rate performance.
引用
收藏
页码：2828 / 2835
页数：8
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