A Multi-Agent Collaborative Model for Bayesian Opportunistic Channel Accessibility in Railway Cognitive Radio

doi:10.23940/ijpe.17.04.p15.479489

Abstract

Abstract:

Applying cognitive radio to railway communication systems is a cutting-edge research area. This paper aims to solve the optimization problem of the global channels opportunistic accessibility in railway cognitive radio environments. In particular, we propose an efficient cooperative model for multiple wayside base stations. This model consists of Bayesian inference to calculate the probability of successful transmission on a single station along with team collaboration to maximize network performance within a group of base stations. Instead of only performing the traditional sensing and assigning, the base stations have an ability to learn from the interactions among others and the environment to gain prior knowledge. The base station agents further analyze prior knowledge and perform optimal channel assignment for global network performance. Using our cooperative model of channels opportunistic accessibility, we have shown that the model can also reduce the computational complexity in high-mobility communication environments.

Submitted on December 27, 2016; Revised on March 2, 2017; Accepted on June 17, 2017
References: 28

Zhijie Yin, Yiming Wang, and Cheng Wu. A Multi-Agent Collaborative Model for Bayesian Opportunistic Channel Accessibility in Railway Cognitive Radio [J]. Int J Performability Eng, 2017, 13(4): 479-489.

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