Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment

11/16/2020
by   Haoran Sun, et al.
0

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, 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.

READ FULL TEXT
research
05/03/2021

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

There has been a growing interest in developing data-driven, and in part...
research
12/03/2018

Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach

Knowledge of information about the propagation channel in which a wirele...
research
03/20/2022

Attention Aided CSI Wireless Localization

Deep neural networks (DNNs) have become a popular approach for wireless ...
research
12/31/2021

Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments

A key challenge for AI is to build embodied systems that operate in dyna...
research
09/21/2023

Meta OOD Learning for Continuously Adaptive OOD Detection

Out-of-distribution (OOD) detection is crucial to modern deep learning a...
research
03/12/2020

Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocation

Multiple-input multiple-output (MIMO) is a key for the fifth generation ...
research
04/30/2022

Operational Adaptation of DNN Classifiers using Elastic Weight Consolidation

Autonomous systems (AS) often use Deep Neural Network (DNN) classifiers ...

Please sign up or login with your details

Forgot password? Click here to reset