Sparse channel estimation in MIMO-OFDM systems based on an improved sparse reconstruction by separable approximation algorithm

作者：Ye Xinrong; Zhu Weiping^*; Zhang Aiqing; Meng Qingmin

来源：Journal of Information and Computational Science, 2013, 10(2): 609-619.

• Binary information
• Estimation process
• In-channels
• Iterative shrinkage-thresholding algorithms
• Least square methods
• MIMO-OFDM
• MIMO-OFDM systems
• Noise interference
• Pilot subcarriers
• Running time
• Separable approximation
• Signaltonoise ratio (SNR)
• Sparse channel estimations
• Sparse reconstruction
• Sparse signals
• Time domain

This paper investigates the sparse channel estimation issue of MIMO-OFDM systems based on compressed sensing. Beginning with a sparse channel estimation model is presented, then, an Improved Sparse Reconstruction by Separable Approximation (ISpaRSA) algorithm is proposed, which is faster than the original SpaRSA. In order to decrease the number of pilots in channel estimation process of MIMOOFDM systems, we transform the estimation of sparse frequency selective fading channel in time-domain into the reconstruction of complex sparse signal in the existence of noise interference in compressed sensing theory, and propose a method of sparse channel estimation based on ISpaRSA. At the same Signal-to-noise Ratio (SNR), comparing with the conventional Least Square (LS) method, a number of computer-simulation-based experiments show that the proposed method can reduce near 40% pilot subcarriers to acquire the same performance of MSE and BER, and can short a half of the running time which the sparse channel estimation method based on SpaRSA needs. 2013 Binary Information Press.

• 出版日期2013
• 单位南京邮电大学; 东南大学