Offline Contextual Bandits for Wireless Network Optimization
This addresses network optimization for mobile service providers facing increasing data traffic and quality expectations, though it appears incremental as it adapts existing methods.
The paper tackles the problem of automatically adjusting wireless network configuration parameters in response to changing user demand, proposing a method that combines existing offline learning techniques to achieve important performance gains while meeting computational efficiency constraints.
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.