基于联合稀疏模型的OFDM线性时变信道估计

为了进一步提高OFDM线性时变信道估计性能,利用信道抽头的时域稀疏特性和相关性,提出一种基于联合稀疏模型的信道估计方法.首先,将线性时变信道模型下对连续多个符号周期的信道估计转换成一个联合稀疏重构模型;其次,采用基于测量矩阵互相关性最小化的分组导频设计准则,在应对子载波干扰的同时,保证了稀疏重构算法的性能;最后,设计一种基于循环并行树的分组导频优化算法.仿真结果表明:与传统线性时变信道估计方法和联合稀疏模型下的信道估计方法相比,所提方法所需导频数量少,信道估计性能更好,同时便于工程应用.

Linear Time-Varying Channel Estimation of OFDM Based on Joint Sparse Model

In order to improve the performance of linear time-varying channel estimation in OFDM system， a channel estimation method based on the joint sparse model was presented by using the temporal sparsity and correlation of channel tap. Firstly， the channel estimation of continuous OFDM system periods was converted to a joint sparse reconstruction model. Secondly， the group pilot based on the minimization of the measurement matrix coherence was used to deal with the subcarrier interference and ensure the performance of the sparse reconstruction algorithm. Finally， a group pilot pattern optimization algorithm based on the parallel tree loop structure was also proposed. Compared with the traditional linear time-varying channel estimation method and the channel estimation method based on the joint sparse model， the proposed method needs less pilot frequency，has much better channel estimation performance， and is convenient for application.