Machine Learning Empowered Emerging Wireless Networks in 6G: Recent Advancements, Challenges and Future Trends

Abstract

The upcoming 6G networks are sixth-sense next-generation communication networks with an ever-increasing demand for enhanced end-to-end (E2E) connectivity towards a connected, sustainable world. Recent developments in artificial intelligence (AI) have enabled a wide range of novel technologies through the availability of advanced machine learning (ML) models, large datasets, and high computational power. In addition, intelligent resource management is a key feature of 6G networks that enables self-configuration and self-healing by leveraging the parallel computing and autonomous decision-making ability of ML techniques to enhance energy efficiency and computational capacity in 6G networks. Consequently, ML techniques will play a significant role in addressing resource management and mobility management challenges in 6G wireless networks. This article provides a comprehensive review of state-of-the-art ML algorithms applied in 6G wireless networks, categorized into learning types, including supervised and unsupervised machine learning, Deep Learning (DL), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL) and Federated Learning (FL). In particular, we review the ML algorithms applied in the emerging networks paradigm, such as device-to-device (D2D) networks, vehicular networks (Vnet), and Fog-Radio Access Networks (F-RANs). We highlight the ML-based solutions to address technical challenges in terms of resource allocation, task offloading, and handover management. We also provide a detailed review of the ML techniques to improve energy efficiency and reduce latency in 6G wireless networks. To this end, we identify the open research issues and future trends concerning ML-based intelligent resource management applications in 6G networks.

Keywords

• 6G
• D2D communication
• energy efficiency
• machine learning
• resource management