Randomized Scheduling of Real-Time Traffic in Wireless Networks Over Fading Channels

TL;DR

This paper develops randomized scheduling algorithms for real-time traffic in wireless networks with fading channels, achieving higher efficiency ratios than classical methods like Max-Weight Scheduling.

Contribution

It introduces randomized algorithms that improve efficiency ratios for real-time wireless scheduling under fading channels and provides low-complexity distributed solutions.

Findings

Randomized algorithms outperform classical scheduling methods.

Max-Weight Scheduling achieves an efficiency ratio of 1/2.

Proposed algorithms achieve efficiency ratios strictly greater than 1/2.

Abstract

Despite the rich literature on scheduling algorithms for wireless networks, algorithms that can provide deadline guarantees on packet delivery for general traffic and interference models are very limited. In this paper, we study the problem of scheduling real-time traffic under a conflict-graph interference model with unreliable links due to channel fading. Packets that are not successfully delivered within their deadlines are of no value. We consider traffic (packet arrival and deadline) and fading (link reliability) processes that evolve as an unknown finite-state Markov chain. The performance metric is efficiency ratio which is the fraction of packets of each link which are delivered within their deadlines compared to that under the optimal (unknown) policy. We first show a conversion result that shows classical non-real-time scheduling algorithms can be ported to the real-time setting and yield a constant efficiency ratio, in particular, Max-Weight Scheduling (MWS) yields an efficiency ratio of 1/2. We then propose randomized algorithms that achieve efficiency ratios strictly higher than 1/2, by carefully randomizing over the maximal schedules. We further propose low-complexity and myopic distributed randomized algorithms, and characterize their efficiency ratio. Simulation results are presented that verify that randomized algorithms outperform classical algorithms such as MWS and GMS.