Envision of mmWave Wireless Communication with Artificial Intelligence

TL;DR

This paper reviews millimeter-wave wireless communication challenges and explores how artificial intelligence and machine learning can address these issues, highlighting recent technological progress and future research directions.

Contribution

It provides a comprehensive analysis of millimeter-wave communication challenges and discusses the integration of AI and machine learning techniques to enhance system performance.

Findings

AI and machine learning improve millimeter-wave signal processing.

Recent advancements address transmission loss and absorption issues.

Future research directions are proposed for AI-enabled mmWave systems.

Abstract

The future wireless communication system faces the bottleneck of the shortage of traditional spectrum resources and the explosive growth of the demand for wireless services. Millimeter-wave communication with spectral resources has become an effective choice for the next generation of wireless broadband cellular communication. However, the transmission path loss is large and oxygen and water molecules absorb Characteristics such as seriousness have brought great challenges to millimeter wave communication, and it is necessary to seek a technical approach different from low-frequency wireless communication. In the analysis of millimeter wave transmission characteristics After the analysis, the research progress of millimeter wave communication technology and the RF front-end is comprehensively analyzed, and the technology of millimeter wave communication is thoroughly analyzed with technical challenges and proposed corresponding research directions. Finally, the advances of artificial intelligence and machine learning are also applied into millimeter wave communication, so we also cover these parts in this paper.