LEARNING TO CONTINUOUSLY OPTIMIZE WIRELESS RESOURCE IN EPISODICALLY DYNAMIC ENVIRONMENT

There has been a growing interest in developing data-driven, in particular deep neural network (DNN) based methods for modern communication tasks. For a few popular tasks such as power control, beamforming, and MIMO detection, these methods achieve state-of-the-art performance while requiring less computational efforts, less channel state information (CSI), etc. However, it is often challenging for these approaches to learn in a dynamic environment where parameters such as CSIs keep changing. This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. Specifically, we consider an "episodically dynamic" setting where the environment changes in "episodes", and in each episode the environment is stationary. We propose a continual learning (CL) framework for wireless systems, which can incrementally adapt the learning models to the new episodes, without forgetting models learned from the previous episodes. Our design is based on a novel min-max formulation which ensures certain "fairness" across different episodes. Finally, we demonstrate the effectiveness of the CL approach by customizing it to a popular DNN based model for power control, and testing using both synthetic and real data.