Role of Deep Learning in Wireless Communications

TL;DR

Deep learning offers a promising data-driven approach to optimize wireless communication systems, often surpassing traditional model-based methods by learning from large datasets and bypassing explicit channel modeling.

Contribution

This paper demonstrates how deep neural networks can be effectively applied to various wireless communication problems, providing significant system-level improvements over traditional methods.

Findings

Deep learning can optimize reconfigurable intelligent surfaces effectively.

Data-driven methods outperform traditional approaches in multiuser beamforming.

End-to-end neural network training improves millimeter wave initial alignment.

Abstract

Traditional communication system design has always been based on the paradigm of first establishing a mathematical model of the communication channel, then designing and optimizing the system according to the model. The advent of modern machine learning techniques, specifically deep neural networks, has opened up opportunities for data-driven system design and optimization. This article draws examples from the optimization of reconfigurable intelligent surface, distributed channel estimation and feedback for multiuser beamforming, and active sensing for millimeter wave (mmWave) initial alignment to illustrate that a data-driven design that bypasses explicit channel modelling can often discover excellent solutions to communication system design and optimization problems that are otherwise computationally difficult to solve. We show that by performing an end-to-end training of a deep neural network using a large number of channel samples, a machine learning based approach can potentially provide significant system-level improvements as compared to the traditional model-based approach for solving optimization problems. The key to the successful applications of machine learning techniques is in choosing the appropriate neural network architecture to match the underlying problem structure.