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-ofthe-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.
|Original language||English (US)|
|Number of pages||5|
|Journal||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|State||Published - 2021|
|Event||2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada|
Duration: Jun 6 2021 → Jun 11 2021
Bibliographical noteFunding Information:
X. Fu and M. Hong are supported by NSF/Intel MLWiNS: CNS-2003082 and CNS-2003033, respectively. T-H. Chang is supported by the NSFC, China, under Grant 62071409 and 61731018.
© 2021 IEEE
- Continual learning
- Data-driven methods
- Deep learning
- Wireless communication