Improved Proportionate Least Mean Square/Fourth Based Channel Equalization for Underwater Acoustic Communications

• Published in: 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
• Main Authors: Tian, Ya-nan, Han, Xiao, Vorobyov, Sergiy A., Li, Weizhe
• Other Authors: Department of Information and Communications Engineering, Sergiy Vorobyov Group, Harbin Engineering University, Aalto-yliopisto, Aalto University
• Format: Text
• Language: English
• Published: 2023
• Subjects: proportional update matrix; sparse level; Underwater acoustic communication; Arctic
• Online Access: https://aaltodoc.aalto.fi/handle/123456789/127666; https://doi.org/10.1109/CAMSAP58249.2023.10403523

Publisher Copyright: © 2023 IEEE. An improved proportionate least mean square/fourth (IPLMS/F) equalizer is proposed in this paper, and applied to underwater acoustic communications in real experiment. In addition to improving the performance of least mean squares (LMS) equalizer, the proposed IPLMS/F equalizer maintains the simplicity and stability of LMS. The advantage of the proposed IPLMS/F equalizer is due to introduction of a proportional update matrix. The diagonal elements of this matrix are determined by the equalizer tap magnitudes to improve the sparsity level estimate, and thus, further improve the equalizer performance. The performance of IPLMS/F is verified by applying it to the experimental data from the 9th Chinese National Arctic Research Expedition. The results show that IPLMS/F exhibits fastest convergence speed and it has the lowest bit error rate compared with LMS and LMS/F, indicating its effectiveness and reliability in practical applications. Peer reviewed