A Review on Machine Learning Applications in Localization in 5G and Beyond Wireless Communications

Abstract

Location is the process of estimating the position of a device or user in a wireless network. Location is critical for many applications and services in 5G and beyond wireless communications. However, localization faces many challenges in complex and dynamic environments, such as multipath propagation, non-line-of-sight (NLOS) conditions, and limited bandwidth and power resources. Machine learning (ML) is a promising technique that can improve location performance and efficiency by exploiting the large amount of data available in wireless networks. In this article, state-of-the-art ML localization techniques are reviewed. In addition, recent ML localization techniques are compared, and observations from the comparison are delineated. Additionally, challenges with possible recommendations are presented.