Fast Link Scheduling in Wireless Networks Using Regularized Off-Policy Reinforcement Learning

Abstract

The centralized-link-scheduling problem in a wireless network graph involves solving the maximum-weighted-independent-set (MWIS) problem on the conflict graph. In this letter, we propose a novel regularized off-policy reinforcement learning-based MWIS solver and use for the scheduling problem. The proposed MWIS algorithm achieves 17% improvement over state-of-the-art heuristic solver KaMIS, 60% over greedy solver, 16% and 17% over RL-based solvers LwD and S2V-DQN, respectively. We show that our scheduler achieves stable throughput values 14% and 22% higher than LwD and a distributed greedy scheduler, respectively. We demonstrate the flexibility of our RL algorithm by modifying it to create a time-since-last-service-aware scheduler.