Delay‐Aware Online Service Scheduling in High‐Speed Railway Communication Systems

Abstract

We investigate the downlink service scheduling problem in relay‐assisted high‐speed railway (HSR) communication systems, taking into account stochastic packet arrivals and quality‐of‐service (QoS) requirements. The scheduling problem is formulated as an infinite‐horizon average cost constrained Markov decision process (MDP), where the scheduling actions depend on the channel state information (CSI) and the queue state information (QSI). Our objective is to find a policy that minimizes the average end‐to‐end delay through scheduling actions under the service delivery ratio constraints. To address the challenge of centralized control and high complexity of traditional MDP approaches, we propose a distributed online scheduling algorithm based on approximate MDP and stochastic learning, where the scheduling policy is a function of the local CSI and QSI only. Numerical experiments are carried out to show the performance of the proposed algorithm.