AUTHOR=Zou Sichen , Jia Shuyang , Zhang Xiaochuan , Liu Baoheng 

TITLE=A fast temporal multiple sparse Bayesian learning-based channel estimation method for time-varying underwater acoustic OFDM systems

JOURNAL=Frontiers in Communications and Networks

VOLUME=Volume 5 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/communications-and-networks/articles/10.3389/frcmn.2024.1465902

DOI=10.3389/frcmn.2024.1465902

ISSN=2673-530X

ABSTRACT=In this paper, a fast temporal multiple sparse Bayesian learning based channel estimation method (FTMSBL) in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system is proposed, which is optimized by fast marginalized likelihood maximization method. The algorithm can make full use of the consistent sparse structure and time-domain correlation properties of channels to improve the reconstruction performance and computational efficiency, which possess both better performance and high computational efficiency than that of traditional Bayesian learning algorithms. At the same time, the FTMSBL algorithm is not required to compute the inverse of large matrices and consumes very little storage resources in the operation, which is suitable for hardware implementation. Simulation and sea trial results show that the FTMSBL-based underwater channel estimation algorithm has higher channel estimation accuracy than the orthogonal matching tracking algorithm, and the system bit error rate is significantly reduced, especially the FTMSBL algorithm can achieve optimal performance in strong time-dependent channels.