Machine Learning-Enabled 5G and 6G Networks: Methods, Challenges, and Opportunities
Source abstract
Abstract
Fifth-generation (5G) and sixth-generation (6G) wireless communications aim to achieve significantly higher data speeds, remarkably low latency, and substantial improvements in the efficiency of base stations. With the rapid increase in the utilization of broadband data driven by Internet of Things (IoT) gadgets, smart home systems, autonomous vehicles, and virtual reality devices, 5G and 6G networks are set to overcome the limitations of earlier telecommunication technologies and serve as key enablers for future IoT applications. Anticipated as the primary infrastructure for delivering emerging services, 5G cellular networks introduce new requirements and challenges that complicate the achievement of desired objectives. This paper provides a comprehensive overview of machine learning (ML) methods and their application in 5G and 6G wireless networks, covering supervised, unsupervised, and reinforcement learning (RL) approaches. ML is set to play a central and important role in 6G systems for these wireless networks. Subsequently, this paper thoroughly explores a series of challenges within the domain of 5G and 6G networks and examines research opportunities for applying ML techniques to address these challenges.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Wireless Communication Technologies · Advanced Data and IoT Technologies · IoT and Edge/Fog Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Auckland Park, P.O. Box 524, Johannesburg 2006, South Africa
References (100+)
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