Compressed Channel Estimation based on Optimized Measurement Matrix

摘要：

Channel estimation which can acquire the channel fading information is a key technology to improve the performance at the receive node in wireless channel transmission. The inherent sparse feature of multipath channel makes the CS theory (compressed sensing) for multipath channel estimation become possible. Traditional linear estimation method does not take the inherent sparse feature of the channel into account, So the reconstruction of compressed sensing for channel estimation has a much better result than that with the traditional method of least square estimation when the training sequence is short, which proves the excellent performance of compressed channel estimation. When applying the compressed sensing theory to sparse channel estimation, by reducing the correlation between column vectors of the measurement matrix to form a optimized measurement matrix, it can lead a further improved performance in sparse channel estimation.

关键词： sparse multipath channel least square estimation compressed channel estimation optimized measurement matrix correlation

作者: Xiaochao Xiao Baoyu Zheng Chenhao Wang

作者单位: Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications University of Posts and Telecommunications Broadband wireless communications and sensor technology University of Posts and Telecommunications Broadband wireless communications and sensor technology

会议类型: 国际会议

会议名称: 2011年无线通信与信号处理国际会议(WCSP 2011)

会议地点: 南京

会议语种:英文

页码: 1-5

在线出版日期: 2011-11-09（万方平台首次上网日期，不代表论文的发表时间）