Artificial Intelligence for 6G Networks: Technology Advancement and Standardization

TL;DR

This paper discusses the role of artificial intelligence, especially machine learning, in advancing 6G wireless networks, focusing on technological challenges, standardization efforts, and future research directions.

Contribution

It provides an overview of AI integration in 6G, examines standardization activities, and highlights key issues and potential solutions for ML in future wireless networks.

Findings

ML can significantly improve physical and link layer performance in 6G.

Standardization bodies are beginning to incorporate ML considerations.

Major challenges include data privacy, model robustness, and integration complexity.

Abstract

With the deployment of 5G networks, standards organizations have started working on the design phase for sixth-generation (6G) networks. 6G networks will be immensely complex, requiring more deployment time, cost and management efforts. On the other hand, mobile network operators demand these networks to be intelligent, self-organizing, and cost-effective to reduce operating expenses (OPEX). Machine learning (ML), a branch of artificial intelligence (AI), is the answer to many of these challenges providing pragmatic solutions, which can entirely change the future of wireless network technologies. By using some case study examples, we briefly examine the most compelling problems, particularly at the physical (PHY) and link layers in cellular networks where ML can bring significant gains. We also review standardization activities in relation to the use of ML in wireless networks and future timeline on readiness of standardization bodies to adapt to these changes. Finally, we highlight major issues in ML use in the wireless technology, and provide potential directions to mitigate some of them in 6G wireless networks.