Block Bayesian Sparse Learning Algorithms With Application to Estimating Channels in OFDM Systems

TL;DR

This paper introduces novel Bayesian block-sparse learning algorithms for estimating cluster-sparse channels in OFDM systems, significantly improving accuracy by exploiting cluster structures even with unknown prior information.

Contribution

It proposes new Bayesian algorithms for cluster-sparse channel estimation in OFDM that do not require prior block partition knowledge, enhancing estimation performance.

Findings

Proposed methods outperform previous algorithms in simulations.

Algorithms effectively utilize cluster correlation in training matrices.

Performance improves even when prior block information is unknown.

Abstract

Cluster-sparse channels often exist in frequencyselective fading broadband communication systems. The main reason is received scattered waveform exhibits cluster structure which is caused by a few reflectors near the receiver. Conventional sparse channel estimation methods have been proposed for general sparse channel model which without considering the potential cluster-sparse structure information. In this paper, we investigate the cluster-sparse channel estimation (CS-CE) problems in the state of the art orthogonal frequencydivision multiplexing (OFDM) systems. Novel Bayesian clustersparse channel estimation (BCS-CE) methods are proposed to exploit the cluster-sparse structure by using block sparse Bayesian learning (BSBL) algorithm. The proposed methods take advantage of the cluster correlation in training matrix so that they can improve estimation performance. In addition, different from our previous method using uniform block partition information, the proposed methods can work well when the prior block partition information of channels is unknown. Computer simulations show that the proposed method has a superior performance when compared with the previous methods.