针对块稀疏信道的估计算法

Novel Channel Estimation Algorithms for Block Sparse Channels

无线多径信道中存在着块稀疏结构。针对块稀疏信道中分块信息是否已知的不同场景,分别提出了两种基于块稀疏贝叶斯学习(BSBL)框架的OFDM系统信道估计算法。这两种算法根据边界最优(BO)方法估计信道分块的稀疏度参数,提升算法运算速率。为进一步提升信道估计性能,在基于BSBL框架算法仅利用导频信号估计信道的基础上,又提出了基于联合块稀疏贝叶斯学习(JBSBL)的信道估计新算法,该算法利用导频与数据子载波实现信道的联合估计。仿真结果表明,与传统的信道估计算法相比,本文提出的算法均可获得很好的信道估计性能,且基于JBSBL的信道估计算法性能更佳。

Wireless multipath channels often exhibit block-sparse structure. This paper addresses the problem of estimating block sparse channels in orthogonal frequency division multiplexing（ OFDM） systems. According to whether the block partition information is available,novel algorithms of block-sparse channel estimation based on block sparse bayesian learning（ BSBL） framework utilizing the sparsity property are proposed. For the purpose of improving the computational speed,the two proposed algorithms use bound optimization（ BO） method to learn the unknown parameters which control the sparsity of block-sparse channels. These algorithms based on BSBL framework only use pilots to estimate channel,we also propose joint BSBL algorithms that both the data and the pilot subcarriers are incorporated to improve estimation performance without decreasing spectrally efficiency. Monte Carlo simulations have shown that the proposed algorithms have better performance than the conventional channel estimation algorithms. By using the joint BSBL algorithm,the performance will be further improved.