Clock Compensation Strategy in Train Ethernet

Abstract

How to degrade the delay time of gateway queuing with heavy network load in train communication networks is the key to guarantee the stability of the train. This article models the queue scheduling problem modeling into reinforcement learning process, puts forward an adaptive weights learning polling scheduling algorithm with the purpose of dynamic scheduling for vehicle gateway node queue. With the compare of algorithm in this paper and weighted round-robin scheduling algorithm in adequate and inadequate bandwidth resources situations, we have approved the superiority of the algorithm in this paper.