Online Deep Neural Network for Optimization in Wireless Communications

TL;DR

This paper introduces an online deep neural network approach for wireless communication optimization, enabling better generalization, interpretability, and performance compared to traditional offline methods and iterative algorithms.

Contribution

The paper presents a novel online DNN framework that trains a dedicated network per data sample, improving generalization and interpretability in wireless system optimization.

Findings

Outperforms offline DNN and iterative algorithms in simulations.

Achieves high performance with low computational complexity.

Demonstrates effectiveness in IRS-aided multi-user MIMO systems.

Abstract

Recently, deep neural network (DNN) has been widely adopted in the design of intelligent communication systems thanks to its strong learning ability and low testing complexity. However, most current offline DNN-based methods still suffer from unsatisfactory performance, limited generalization ability, and poor interpretability. In this article, we propose an online DNN-based approach to solve general optimization problems in wireless communications, where a dedicated DNN is trained for each data sample. By treating the optimization variables and the objective function as network parameters and loss function, respectively, the optimization problem can be solved equivalently through network training. Thanks to the online optimization nature and meaningful network parameters, the proposed approach owns strong generalization ability and interpretability, while its superior performance is demonstrated through a practical example of joint beamforming in intelligent reflecting surface (IRS)-aided multi-user multiple-input multiple-output (MIMO) systems. Simulation results show that the proposed online DNN outperforms conventional offline DNN and state-of-the-art iterative optimization algorithm, but with low complexity.