Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

Abstract

Fifth-generation (5G) technology will play a vital role in future wireless networks. The breakthrough 5G technology will unleash a massive Internet of Everything (IoE), where billions of connected devices, people, and processes will be simultaneously served. The services provided by 5G include several use cases enabled by the enhanced mobile broadband, massive machine-type communications, and ultra-reliable low-latency communication. Fifth-generation networks potentially merge multiple networks on a single platform, providing a landscape for seamless connectivity, particularly for high-mobility devices. With their enhanced speed, 5G networks are prone to various research challenges. In this context, we provide a comprehensive survey on 5G technologies that emphasize machine learning-based solutions to cope with existing and future challenges. First, we discuss 5G network architecture and outline the key performance indicators compared to the previous and upcoming network generations. Second, we discuss next-generation wireless networks and their characteristics, applications, and use cases for fast connectivity to billions of devices. Then, we confer physical layer services, functions, and issues that decrease the signal quality. We also present studies on 5G network technologies, 5G propelling trends, and architectures that help to achieve the goals of 5G. Moreover, we discuss signaling techniques for 5G massive multiple-input and multiple-output and beam-forming techniques to enhance data rates with efficient spectrum sharing. Further, we review security and privacy concerns in 5G and standard bodies’ actionable recommendations for policy makers. Finally, we also discuss emerging challenges and future directions.

Interplay between Physical Layer Security and Blockchain Technology for 5G and Beyond: A Comprehensive Survey

“…It is expected that the integration of AI at all levels of xG networks will go much deeper to ensure efficient, adaptive, and eco-friendly deployments and management. Thus far, AI-and ML in particular-has been shown to improve physical layer channel coding [215], obstacle detection [216], and PLS [217]. Research in these areas is still in its early stages, and further investigation is needed.…”

Interplay between Physical Layer Security and Blockchain Technology for 5G and Beyond: A Comprehensive Survey

“…It is expected that the integration of AI at all levels of xG networks will go much deeper to ensure efficient, adaptive, and eco-friendly deployments and management. Thus far, AI-and ML in particular-has been shown to improve physical layer channel coding [238], obstacle detection [239], and PLS [240]. Research in these areas is still in its early stages, and further investigation is needed.…”

A review of applications of supervised learning to future networks

“…All wireless devices communicate by radio waves with a cellular base station via fixed antennas, over frequency channels assigned by the base station. For 5G networks, the antennas transmit radio waves at shorter wavelengths (mmWaves), which have a larger frequency (30-300 GHz), and therefore greater peak data rates (~10 Gbit/s) compared with 4G (Tanveer et al, 2021). The disadvantage is that mmWaves have a shorter range, so higher gain antennas are needed, due to the increased loss.…”

“…In section 3, we review ML techniques applied to wireless networks, with emphasis on 5G technologies. The examples presented in this section are taken from a comprehensive literature review (Kulin et al, 2021), along with (Ly and Yao, 2021;Santos et al, 2020;Tanveer et al, 2021) which focus on 5G technologies. In section 4, we review applications of ML to optical networks, with examples taken from the literature reviews (Gu et al, 2020;Mata et al, 2018).…”