Delay Optimal Scheduling of Arbitrarily Bursty Traffic over Multi-State Time-Varying Channels

TL;DR

This paper proposes a cross-layer scheduling policy for bursty traffic over multi-state channels, optimizing delay and power tradeoff by converting a complex non-linear problem into a linear program, resulting in an optimal threshold-based solution.

Contribution

It introduces a probabilistic, threshold-based scheduling policy derived from a Markov chain model to optimize delay under power constraints in multi-state channels.

Findings

Optimal threshold-based scheduling policy derived

Delay-power tradeoff characterized through linear programming

Cross-layer approach improves queueing delay performance

Abstract

In this paper, we study joint queue-aware and channel-aware scheduling of arbitrarily bursty traffic over multi-state time-varying channels, where the bursty packet arrival in the network layer, the backlogged queue in the data link layer, and the power adaptive transmission with fixed modulation in the physical layer are jointly considered from a cross-layer perspective. To achieve minimum queueing delay given a power constraint, a probabilistic cross-layer scheduling policy is proposed, and characterized by a Markov chain model. To describe the delay-power tradeoff, we formulate a non-linear optimization problem, which however is very challenging to solve. To handle with this issue, we convert the optimization problem into an equivalent Linear Programming (LP) problem, which allows us to obtain the optimal threshold-based scheduling policy with an optimal threshold imposed on the queue length in accordance with each channel state.