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

Abstract EN

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.