Adaptive Mechanism for Distributed Opportunistic Scheduling
For wireless network researchers, this work provides a practical adaptive mechanism for DOS that balances stability and convergence speed, though it is an incremental improvement over existing DOS approaches.
The paper addresses the challenge of jointly optimizing access probability and channel quality threshold in Distributed Opportunistic Scheduling (DOS) to maximize proportional fairness throughput. It proposes an adaptive control-theoretic algorithm that converges to the optimal configuration, validated by simulations showing improved throughput over prior methods.
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.