Throughput-Optimal Opportunistic Scheduling in the Presence of Flow-Level Dynamics

TL;DR

This paper introduces a new workload-based scheduling algorithm that is proven to be throughput-optimal in wireless networks with flow-level dynamics, outperforming existing methods without requiring prior knowledge of channels or demands.

Contribution

The paper proposes a novel workload-based scheduling with learning algorithm that achieves throughput-optimality in dynamic wireless networks, overcoming limitations of the MaxWeight algorithm.

Findings

Proposed algorithm is throughput-optimal.

Requires no prior knowledge of channels or demands.

Performs significantly better than previous algorithms.

Abstract

We consider multiuser scheduling in wireless networks with channel variations and flow-level dynamics. Recently, it has been shown that the MaxWeight algorithm, which is throughput-optimal in networks with a fixed number users, fails to achieve the maximum throughput in the presence of flow-level dynamics. In this paper, we propose a new algorithm, called workload-based scheduling with learning, which is provably throughput-optimal, requires no prior knowledge of channels and user demands, and performs significantly better than previously suggested algorithms.