Collaborative spatial reuse in wireless networks via selfish multi-armed bandits
Published in Unknown Venue, 2019
Recommended citation: Francesc Wilhelmi and Cristina Cano and Gergely Neu and Boris Bellalta and Anders Jonsson and Sergio Barrachina-Muñoz (2019). Collaborative spatial reuse in wireless networks via selfish multi-armed bandits. Unknown Venue. https://www.sciencedirect.com/science/article/pii/S1570870518302646
Abstract: Next-generation wireless deployments are characterized by being dense and uncoordinated, which often leads to inefficient use of resources and poor performance. To solve this, we envision the utilization of completely decentralized mechanisms to enable Spatial Reuse (SR). In particular, we focus on dynamic channel selection and Transmission Power Control (TPC). We rely on Reinforcement Learning (RL), and more specifically on Multi-Armed Bandits (MABs), to allow networks to learn their best configuration. In this work, we study the exploration-exploitation trade-off by means of the ε-greedy, EXP3, UCB and Thompson sampling action-selection, and compare their performance. In addition, we study the implications of selecting actions simultaneously in an adversarial setting (i.e., concurrently), and compare it with a sequential approach. Our results show that optimal proportional fairness can be achieved …