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In the context of railway systems, the application performance can be verycritical and the radio conditions not advantageous. Hence, the communicationproblem parameters include both a survival time stemming from the applicationlayer and a channel error probability stemming from the PHY layer. This paperproposes to consider the framework of Markov Decision Process (MDP) to design astrategy for scheduling radio resources based on both application and PHY layerparameters. The MDP approach enables to obtain the optimal strategy via thevalue iteration algorithm. The performance of this algorithm can thus serve asa benchmark to assess lower complexity schedulers. We show numericalevaluations where we compare the value iteration algorithm with otherschedulers, including one based on deep Q learning.

An MDP approach for radio resource allocation in urban Future Railway Mobile Communication System (FRMCS) scenarios