Adaptive Mechanism for Distributed Opportunistic Scheduling

TL;DR

This paper introduces an adaptive distributed opportunistic scheduling mechanism for wireless networks that optimizes throughput and fairness by dynamically adjusting access probabilities and thresholds using control theory.

Contribution

It presents a novel adaptive algorithm based on control theory to optimize and stabilize distributed opportunistic scheduling parameters in wireless networks.

Findings

The proposed mechanism achieves higher throughput than previous methods.

The adaptive algorithm converges reliably to optimal operating points.

Simulation results confirm the effectiveness and stability of the approach.

Abstract

Distributed Opportunistic Scheduling (DOS) techniques have been recently proposed to improve the throughput performance of wireless networks. With DOS, each station contends for the channel with a certain access probability. If a contention is successful, the station measures the channel conditions and transmits in case the channel quality is above a certain threshold. Otherwise, the station does not use the transmission opportunity, allowing all stations to recontend. A key challenge with DOS is to design a distributed algorithm that optimally adjusts the access probability and the threshold of each station. To address this challenge, in this paper we first compute the configuration of these two parameters that jointly optimizes throughput performance in terms of proportional fairness. Then, we propose an adaptive algorithm based on control theory that converges to the desired point of operation. Finally, we conduct a control theoretic analysis of the algorithm to find a setting for its parameters that provides a good tradeoff between stability and speed of convergence. Simulation results validate the design of the proposed mechanism and confirm its advantages over previous proposals.