Offline Contextual Bandits for Wireless Network Optimization

TL;DR

This paper introduces an offline learning approach for optimizing wireless network configurations, enabling automatic adjustments based on user demand to improve performance efficiently.

Contribution

It adapts offline contextual bandit algorithms specifically for wireless network optimization, addressing practical deployment challenges.

Findings

Achieves significant performance improvements in simulated network scenarios.

Demonstrates computational efficiency suitable for real-world deployment.

Outperforms existing methods in offline policy learning for networks.

Abstract

The explosion in mobile data traffic together with the ever-increasing expectations for higher quality of service call for the development of AI algorithms for wireless network optimization. In this paper, we investigate how to learn policies that can automatically adjust the configuration parameters of every cell in the network in response to the changes in the user demand. Our solution combines existent methods for offline learning and adapts them in a principled way to overcome crucial challenges arising in this context. Empirical results suggest that our proposed method will achieve important performance gains when deployed in the real network while satisfying practical constrains on computational efficiency.