Paper Title
Sparse Channel Estimation and Equalization for Underwater Communication Channels

Abstract
The underwater communication channel has been usually modeled as sparse. Sparse multipath channels are wireless links in digital communication systems [3,4].With increasing advancement in sparse channel estimation, new estimation techniques have been proposed and implemented to estimate the channel parameters accurately [57]. In this work, we first demonstrate the Least Square (LS) for channel estimation however, werequire longer trainingto find accurate estimate of the sparse channel.Subsequently,to finds a sequence of approximations, an algorithm called Matching Pursuit (MP) has been proposed which are also sparse by nature [8,3]. However, correcting mistakes can take a lot of time via MP algorithm[9]. Furthermore, we utilized Least Mean Square (LMS) and its extensions which leads to minimum mean square error (MMSE). In this paper, we discuss some estimation techniques with equalizer which shows sparse nature of channel. A comprehensive analysis is presented and compared different estimation techniques to observe the sparsity of multipath channels. In addition, we also demonstrate a comparative analysis of adaptive estimation algorithms such as LMS, NLMS, and VSSLMS for both sparse and non-sparse multipath channels.In wireless communication system, several algorithms have been proposed for the accurate channel estimation. To observe the sparse nature of channel, Matching pursuit algorithm has performed batter while LS has good performance for non-sparse channel. As we increase the value of SNR, we got improved BER and NCMSE and less error. On the other hand, VSSLMS has outperformed for non-sparse channelwhile,LMS outperforms in case of sparse channelas compared to others due to less bit error rate for a typical value of SNR. Keywords - Underwater Acoustic communication, wireless channels, sparse estimation