Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions

TL;DR

This paper reviews the application of deep learning techniques to 5G wireless communication, highlighting new frameworks for NOMA, MIMO, and mmWave that improve performance and address existing limitations.

Contribution

It introduces novel deep learning-based communication schemes for 5G, demonstrating their potential to enhance system performance and overcome traditional communication constraints.

Findings

Deep learning improves 5G communication efficiency.

New frameworks for NOMA, MIMO, and mmWave are proposed.

Deep learning-based physical layer designs outperform conventional methods.

Abstract

The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, the current communication systems, which were designed on the basis of conventional communication theories, signficantly restrict further performance improvements and lead to severe limitations. Recently, the emerging deep learning techniques have been recognized as a promising tool for handling the complicated communication systems, and their potential for optimizing wireless communications has been demonstrated. In this article, we first review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios. Specifically, the key ideas for several important deep learningbased communication methods are presented along with the research opportunities and challenges. In particular, novel communication frameworks of non-orthogonal multiple access (NOMA), massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are investigated, and their superior performances are demonstrated. We vision that the appealing deep learning-based wireless physical layer frameworks will bring a new direction in communication theories and that this work will move us forward along this road.