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27 November 2019 An offline fast model training method using CGAN for anti-jamming in true environment
Minmin Jiang, Da-peng Li, Fuqi Mu, Xin Qiu, Xurong Chai, Zhihao Sun
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Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 1132106 (2019) https://doi.org/10.1117/12.2538420
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China
Abstract
Deep learning is applied in the Cognitive Radio field, such as anti-jamming communication based on spectrum waterfall image. However, the anti-jamming communication is a decision problem essentially, which interacts with the environment dynamically and is quite different from the problems of classification and detection. The training for decision problem often requires sampling a large number of labeled dataset from true environment, which undoubtedly suffers from the extremely high time consumption problem. In this paper, an offline fast model training method for anti-jamming communication is proposed. Specifically, a novel framework is developed to accelerate the training procedure. Like the Gym toolkit, a virtual environment generator for the production of the spectrum waterfall image is made by CGAN which is trained using the dataset sampled randomly by real transceiver. Due to the variety of the synthesized spectrum waterfall images outputted rapidly through the environment generator, the training efficiency is improved significantly. The experiments show, compared with the online training method, the time cost of the offline method is reduced over 50%.
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Minmin Jiang, Da-peng Li, Fuqi Mu, Xin Qiu, Xurong Chai, and Zhihao Sun "An offline fast model training method using CGAN for anti-jamming in true environment", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 1132106 (27 November 2019); https://doi.org/10.1117/12.2538420
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KEYWORDS
Transceivers
Receivers
Image processing
RF communications
Transmitters
Environmental sensing
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