Intelligent anti-jamming communication technology with electromagnetic spectrum feature cognition
Hui Zhao, Guobin Zhao, Xichun Wang, Zhonghui Zhang, Xianchao Xun
TL;DR
This paper introduces an intelligent anti-jamming communication method using deep learning and game theory to improve signal transmission in complex electromagnetic environments.
Contribution
The novel approach combines deep neural networks and game theory for dynamic anti-jamming communication strategies.
Findings
The model achieves 95.23% accuracy in identifying interference signals.
It outperforms random forest and deep Q-network models with 85.47% anti-interference accuracy.
The method reduces error rates and improves adaptability in dynamic environments.
Abstract
Against the backdrop of the rapid development of wireless communication technology, the complex signal interference issues in the electromagnetic spectrum environment have become a key factor affecting the quality and reliability of signal transmission. Existing solutions, such as traditional interference suppression techniques that rely on static spectrum allocation and fixed interference patterns, are no longer able to adapt to the rapidly changing electromagnetic environment and face computational complexity challenges when processing large amounts of real-time data. This study proposes an intelligent anti-interference algorithm that combines deep neural networks and game theory, and constructs a model based on near-end strategy optimization. By extracting and processing signal features through deep neural networks, and dynamically adjusting communication strategies with near-end…
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Taxonomy
TopicsWireless Signal Modulation Classification · Full-Duplex Wireless Communications · Advanced Data and IoT Technologies