  There has been a growing interest in developing data-driven and 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 to
build the notion of continual learning (CL) into the modeling process of
learning wireless systems, so that the learning model can incrementally adapt
to the new episodes, {\it without forgetting} knowledge learned from the
previous episodes. Our design is based on a novel min-max formulation which
ensures certain ``fairness" across different data samples. We demonstrate the
effectiveness of the CL approach by customizing it to two popular DNN based
models (one for power control and one for beamforming), and testing using both
synthetic and real 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 maintains high performance over the previously encountered
scenarios as well.
