Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Abstract

The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.

6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges

“…With 6G technology, a wide array of possibilities unfolds across three key services: enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC) [ 65 ]. The above use cases are described in detail below: Enhanced mobile broadband (eMBB): 6G networks are expected to further improve upon the enhanced mobile broadband capabilities of 5G by delivering even higher data rates, lower latency, and increased capacity [ 66 ].…”

“…With 6G technology, a wide array of possibilities unfolds across three key services: enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC) [65]. The above use cases are described in detail below:…”

A Federated Learning-Based Resource Allocation Scheme for Relaying-Assisted Communications in Multicellular Next Generation Network Topologies

“…The overarching goal of B5G/6G networks is to evolve "connected intelligence" by deploying data-aided models across a wide span of tasks, applications, and open systems interconnection (OSI) levels. This marks a pivotal shift from mere connectivity to intelligent interactivity, shaping the trajectory of wireless communication networks toward an era characterized by enhanced responsiveness and interconnected intelligence [10][11][12].…”

Sixth-Generation (6G) Networks for Improved Machine-to-Machine (M2M) Communication in Industry 4.0

“…Given the high load and computational complexity, technologies are already working to provide 6G communications to mass users: IRS, unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), etc. Optimizing the functionality of a 6G system may be difficult or even impossible using traditional mathematical methods-instead, machine learning (ML), deep learning (DL), and reinforcement learning (RL) are increasingly being proposed (Figure 1) [2].…”