Delay-Optimal Buffer-Aware Scheduling with Adaptive Transmission

TL;DR

This paper develops a delay-optimal, buffer-aware scheduling policy for communication systems that balances power consumption and delay, using a Markov decision process framework and threshold-based policies.

Contribution

It introduces a convex, piecewise linear delay-power tradeoff curve and proves the optimality of threshold-based policies for this problem.

Findings

Optimal policies are deterministic and threshold-based.

The delay-power tradeoff curve is convex and piecewise linear.

The proposed algorithm efficiently finds optimal policies.

Abstract

In this work, we aim to obtain the optimal tradeoff between the average delay and the average power consumption in a communication system. In our system, the arrivals occur at each timeslot according to a Bernoulli arrival process and are buffered at the transmitter. The transmitter determines the scheduling policy of how many packets to transmit under an average power constraint. The power is assumed to be an increasing and convex function of the number of packets transmitted in each timeslot to capture the realism in communication systems. We also consider a finite buffer and allow the scheduling decision to depend on the buffer occupancy. This problem is modelled as a Constrained Markov Decision Process (CMDP). We first prove that the optimal policy of the (Lagrangian) relaxation of the CMDP is deterministic and threshold-based. We then show that the optimal delay-power tradeoff curve is convex and piecewise linear, where each of the vertices are obtained by the optimal solution to the relaxed problem. This allows us to show the optimal policies of the CMDP are threshold-based, and hence can be implemented by a proposed efficient algorithm. The theoretical results and the algorithm are validated by Linear Programming and simulations.