| Paper ID | SPCOM-9.2 |
| Paper Title |
Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment |
| Authors |
Haoran Sun, University of Minnesota, United States; Wenqiang Pu, Minghe Zhu, The Chinese University of Hong Kong, Shenzhen, China; Xiao Fu, Oregon State University, United States; Tsung-Hui Chang, The Chinese University of Hong Kong, Shenzhen, China; Mingyi Hong, University of Minnesota, United States |
| Session | SPCOM-9: Online and Active Learning for Communications |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Signal Processing for Communications and Networking: [SPC-ML] Machine Learning for Communications |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
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. |
