Application of deep learning technology in performance optimization of communication system

Fang Guo; Jun Guo

doi:10.1117/12.3054423

11 December 2024 Application of deep learning technology in performance optimization of communication system

Fang Guo, Jun Guo

Proceedings Volume 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024); 1344503 (2024) https://doi.org/10.1117/12.3054423
Event: International Conference on Electronics. Electrical and Information Engineering (ICEEIE 2024), 2024, Haikou, China

Abstract

This paper summarizes the key application and importance of DL (Deep Learning) technology in communication system performance optimization. First of all, it emphasizes the key role of communication system in modern society and economy, and the challenges brought by the increasing communication demand. In addition, the multiple applications of DL in communication systems are emphasized, including signal processing and beamforming, channel estimation and equalization, resource allocation and power control. DL model can also be used to detect network intrusion and malicious attacks. In this experiment, DL model is compared with traditional methods in network intrusion detection rate. At different time points, the detection rate of DL model fluctuated, but remained at a high level on the whole. At 12 hours, the detection rate is close to 0.9. Compared with DL model, the detection rate of traditional method fluctuates greatly, and it is obviously lower than DL model at some time points. At the time point of 12 hours, the detection rate of traditional methods is about 0.7. It can be seen that DL model can effectively detect network intrusion and malicious attacks at different time points and has better performance than traditional methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fang Guo and Jun Guo "Application of deep learning technology in performance optimization of communication system", Proc. SPIE 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024), 1344503 (11 December 2024); https://doi.org/10.1117/12.3054423

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KEYWORDS

Telecommunications

Machine learning

Neural networks

Systems modeling

Signal to noise ratio

Performance modeling

Signal processing

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