Recently, there has been a growing interest in estimation of sparse channels as they are observed in underwater acoustic and ultrawideband channels. In this paper we present a new Bayesian sparse channel estimation (SCE) algorithm that, unlike traditional SCE methods, exploits noise statistical information to improve the estimates. The proposed method uses approximate maximum a posteriori proba...

Sparse adaptive channel estimation problem is one of the most important topics in broadband wireless communications systems due to its simplicity and robustness. So far many sparsity-aware channel estimation algorithms have been developed based on the well-known minimum mean square error (MMSE) criterion, such as the zero-attracting least mean square (ZALMS),which are robust under Gaussian assu...

Two-way relay network (TWRN) was introduced to realize high-data rate transmission over the wireless frequency-selective channel. However, TWRC requires the knowledge of channel state information (CSI) not only for coherent data detection but also for the self-data removal. This is partial accomplished by training sequence-based linear channel estimation. However, conventional linear estimation...

Pilot-assisted channel estimation has been investigated to improve the performance of OFDM based LTE systems. LS and MMSE method do not perform excellently because they do not consider the inherent sparse feature of wireless channel. The sparse feature of channel impulse response satisfies the requirement of using compressive sensing (CS) theory, which has recently gained much attention in sign...

MIMO-OFDM systems provide high spectral efficiency for wireless communication systems. However, they have a major drawback of high PAPR which results in inefficient use of a power amplifier and also improper detections. Now a days, in wireless communication systems, channel estimation is mandatory for higher data rates with low bit error rates. For reducing burden on system the channel estimati...

Many wireless channel behavior exhibits approximate sparse modeling in time domain, therefore compressive sensing (CS) approaches are applied for more accurate wireless channel estimation than traditional least squares approach. However, the CS approach is not applied for multicarrier data information recovery because the transmitted symbol can be sparse neither in time domain nor in frequency ...

1. Advanced communication techniques of sparse channel sensing and nonbinary LDPC coding. Underwater acoustic channels are naturally sparse, but how to effectively exploit the sparsity is a challenging task. We will investigate the recently developed “compressive sensing” algorithms for sparse channel estimation in the context of multicarrier acoustic communications. On the other hand, channel ...

1. Advanced communication techniques of sparse channel sensing and nonbinary LDPC coding. Underwater acoustic channels are naturally sparse, but how to effectively exploit the sparsity is a challenging task. We will investigate the recently developed “compressive sensing” algorithms for sparse channel estimation in the context of multicarrier acoustic communications. On the other hand, channel ...

This paper studies normalized least mean square-based adaptive sparse filtering algorithms for estimating multiple-input multiple-output (MIMO) channels. Although the MIMO channel is often modeled as sparse, traditional normalized least mean square-based filtering algorithm never takes the advantage of the inherent sparse structure information and thus causes some performance loss. Unlike the t...

1. Advanced communication techniques of sparse channel sensing and nonbinary LDPC coding. Underwater acoustic channels are naturally sparse, but how to effectively exploit the sparsity is a challenging task. We will investigate the recently developed “compressive sensing” algorithms for sparse channel estimation in the context of multicarrier acoustic communications. On the other hand, channel ...