Machine Learning in Communications: A Road to Intelligent Transmission and Processing

TL;DR

This paper discusses how machine learning transforms wireless communications into more adaptive, efficient, and intelligent systems, overcoming traditional limitations in handling complex, large-scale, and dynamic data problems.

Contribution

It provides an overview of the roles, features, challenges, and practical considerations of machine learning in advancing wireless communication systems.

Findings

Machine learning enhances adaptability and efficiency in wireless communications.

Traditional methods face limitations with large-scale, complex data.

AI-driven systems enable more intelligent information transmission.

Abstract

Prior to the era of artificial intelligence and big data, wireless communications primarily followed a conventional research route involving problem analysis, model building and calibration, algorithm design and tuning, and holistic and empirical verification. However, this methodology often encountered limitations when dealing with large-scale and complex problems and managing dynamic and massive data, resulting in inefficiencies and limited performance of traditional communication systems and methods. As such, wireless communications have embraced the revolutionary impact of artificial intelligence and machine learning, giving birth to more adaptive, efficient, and intelligent systems and algorithms. This technological shift opens a road to intelligent information transmission and processing. This overview article discusses the typical roles of machine learning in intelligent wireless communications, as well as its features, challenges, and practical considerations.