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There has been a growing interest in developing data-driven, and in particular deep neural network (DNN) based methods for modern communication tasks. These methods achieve state-of-the-art performance for a few popular wireless resource allocation problems, while requiring less computational efforts, less resources for acquiring channel state information (CSI), etc. However, it is often challenging for these approaches to learn in a dynamic environment. This work develops a new approach that enables data-driven methods to continuously learn and optimize wireless resource allocation in a dynamic environment. Specifically, we consider an &#x201C;episodically dynamic&#x201D; setting where the environment statistics change in &#x201C;episodes,&#x201D; and in each episode the environment is stationary. We propose to build the notion of continual learning (CL) into wireless system design, so that the learning model can incrementally adapt to the new episodes, <italic>without forgetting</italic> knowledge learned from the previous episodes. We demonstrate the effectiveness of the CL approach by integrating it with three popular DNN based models for power control, beamforming and multi-user MIMO, respectively, and testing using both synthetic and ray-tracing based data sets. These numerical results show that the proposed CL approach is not only able to adapt to the new scenarios quickly and seamlessly, but importantly, it also maintains high performance over the previously encountered scenarios as well.

Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective