Sparse channel estimation for filtered multi‐tone in time domain and subband domain based on matched filtering demodulation. Issue 18 (1st November 2019)

• Record Type: Journal Article
• Authors: Qu, Fengzhong; Zhang, Minhao; Wang, Zhenduo; Qian, Caijie; Lu, Xuesong; Wei, Yan
• Is Part Of: IET communications. Volume 13:Issue 18(2019)
• Page Start: 2889
• Page End: 2894
• Publication Date: 2019-11-01
• Subjects: time‐domain analysis -- fading channels -- OFDM modulation -- demodulation -- equalisers -- matched filters -- error statistics -- channel estimation -- underwater acoustic communication; sparse channel estimation -- time domain -- subband domain -- matched filtering demodulation -- filtered multitone symbols -- frequency selective fading channel -- underwater acoustic channel -- equalisation -- channel sparsity -- FMT channel estimation performance -- received FMT signal -- FMT symbols -- channel estimation matrix -- performance than least square channel estimation; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2018.5931
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : The interference between filtered multi‐tone (FMT) symbols exists and becomes conspicuous especially in frequency selective fading channel, like an underwater acoustic channel. This leads to the necessity of channel estimation and equalisation. In this study, the authors consider two approaches to utilise the channel sparsity to improve FMT channel estimation performance and further reduce bit error rate in underwater acoustic communications. While the first studies FMT sparse channel estimation in the time domain and is based on received FMT signal and known transmitted FMT signal that is reconstructed by pilots, the second is based on matched filtering demodulated FMT symbols and pilots. Theoretical analysis is performed followed by simulation and experiment accordingly. It is found that the first method is able to successfully estimate the sparse channel while the estimation performance of the second method is unsatisfactory due to poor orthogonality between bases of the channel estimation matrix. Moreover, the lake experiment is carried out with the first method and its results prove that sparse channel estimation has better performance than least square channel estimation.