Continuous wave mud pulse channel equalization method based on NARX neural network

Junzhuang Zhang; Zhidan Yan; Tingting Song; Zuodan Wang

doi:10.1117/12.3054410

3 January 2025 Continuous wave mud pulse channel equalization method based on NARX neural network

Junzhuang Zhang, Zhidan Yan, Tingting Song, Zuodan Wang

Author Affiliations +

Proceedings Volume 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024); 134420U (2025) https://doi.org/10.1117/12.3054410
Event: Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 2024, Kaifeng, China

Abstract

The continuous wave mud channel is an extremely complex channel, and the signal is transmitted with phenomena such as inter-code crosstalk, which leads to a decrease in the correct rate of information transmission. Channel equalizers can effectively solve problems such as inter-code crosstalk. Compared with the traditional channel equalizer, the neural network equalizer has a better equalization effect, but the NARX neural network has a slow convergence speed and tends to fall into local optimal solutions. To solve the above problems, a method based on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to optimize NARX neural network is proposed. First, the initial weights of the NARX neural network are optimized using PSO to improve the convergence speed of the NARX neural network, followed by the use of an improved GA to determine the topology of the NARX network at the arrival of the signal in each frame. Then the equalization effects are compared with those of BP, PSO-BP and PSO-NARX equalizers. The final results show that the GAPSO-NARX equalizer has a lower BER, which effectively improves the correctness of information transmission in continuous wave mud channels.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junzhuang Zhang, Zhidan Yan, Tingting Song, and Zuodan Wang "Continuous wave mud pulse channel equalization method based on NARX neural network", Proc. SPIE 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 134420U (3 January 2025); https://doi.org/10.1117/12.3054410

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