OFDM稀疏信道估计中基于树状随机搜索导频设计新方法

A New Pilot Design Method based on Tree-based Stochastic Search for OFDM Sparse Channel Estimation

  • 摘要: 压缩感知(CS,Compressed Sensing)是一种以低速率对稀疏信号进行采样后在接收端重建信号的技术,基于CS的稀疏信道估计具有更小的导频开销且具有更好的信道估计性能。针对基于CS的OFDM稀疏信道估计中的导频设计问题,提出一种基于树状随机搜索算法(TSS,Tree-based Stochastic Search Algorithm)的导频位置设计新方法,该方法结合了树的结构,以分支的方式进行随机搜索从而避免陷入局部最优问题。仿真结果表明,与传统的导频设计方法相比,使用TSS算法获得的导频图案用于信道估计中能够获得更优的信道估计性能,而且TSS算法的复杂度更低。

     

    Abstract:  Compressed Sensing (CS) is a technique of sampling a sparse signal at a low rate and reconstructing it at the receiving end. The CS-based sparse channel estimation has smaller pilot overhead and better channel estimation performance. For the pilot design problem in CS-based OFDM sparse channel estimation, a new pilot location design method based on Tree-based Stochastic Search Algorithm (TSS) is proposed. Inspired by the structure of the tree,the proposed method performs a random search in the manner of branch tree to avoid falling into local optimum. Simulation results show that, compared with the traditional pilot design method, the pilot location obtained by TSS algorithm with lower complexity can obtain better channel estimation performance.

     

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