Bayesian Sequential Learning for Railway Cognitive Radio
Applying cognitive radio in the railway communication systems is a cutting-edge research area. The rapid motion of the train makes the spectrum access of the railway wireless environment instable. To address the issue, first we formulate the spectrum management of railway cognitive radio as a distributed sequential decision problem. Then, based on the available environmental information, we propose a multi-cognitive-base-station cascade collaboration algorithm by using naive Bayesian learning and agent theory. Finally, our experiment results reveal that the model can improve the performance of spectrum access. This cognitive-base-station multi-agent system scheme comprehensively solves the problem of low efficiency in the dynamic access of the railway cognitive radio. The article is also a typical case of artificial intelligence applied in the field of the smart city.
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