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A LEARNING-BASED RANDOM ACCESS METHOD USING MULTI-AGENT MULTI-ARMED BANDIT ALGORITHMS ON WIRELESS COMMUNICATION NETWORKS
A LEARNING-BASED RANDOM ACCESS METHOD USING MULTI-AGENT MULTI-ARMED BANDIT ALGORITHMS ON WIRELESS COMMUNICATION NETWORKS
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机译:基于学习的无线通信网络上的多臂多武装频率算法的随机访问方法
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摘要
The present invention is a multi-agent multi-slot machine of reinforcement learning, a method of machine learning, when N (N>1) nodes try to transmit a data frame at the same time in a wireless communication network. Using the Bandit, MAB) algorithm, each node divides the time each node attempts to transmit in a time synchronization system consisting of time slots having a certain size, and learns an optimal method that does not collide with each other It is a random access protocol.
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