Speaker: Prof. Alexandre Proutière (KTH)
Place: Room TD-C Chappe/Lamarr, 6 avenue des arts, La Doua Campus
Title: Radio Network Optimization: A Bandit Approach
Abstract: In this talk, we demonstrate how to efficiently solve radio network optimization problems using a bandit optimization framework. We mainly consider the problem of controlling antenna tilts in cellular networks (so as to reach an efficient trade-off between network coverage and capacity). We start with the design of algorithms learning optimal antenna tilt control policies at a single base station, and formalize this design as a Best Policy Identification (BPI) problem in contextual Multi-Arm Bandits (MABs). We then consider coordinated antenna tilt policies at several interfering base stations, and formalize the design of algorithms learning such policies as a multi-agent MAB problem. In both settings, we derive information-theoretical performance upper bounds satisfied by any algorithm, and devise algorithms approaching these fundamental limits. We illustrate our results numerically using both synthetic and real-world experiments.
This is a joint work with Filippo Vannella (KTH / Ericsson Research) and Jaeseong Jeong (Ericsson Research). The talk is based on the following papers:
https://arxiv.org/pdf/2201.02169.pdf (IEEE Infocom 2022)
https://proceedings.mlr.press/v202/vannella23a/vannella23a.pdf (ICML 2023)
Statistical and computational trade-off in multi-agents multi-armed bandits (to appear in NeurIPS 2023)