Ten Challenges in Advancing Machine Learning Technologies toward 6G

By Nei Kato, Bomin Mao, Fengxiao Tang, Yuichi Kawamoto, Jiajia Liu. IEEE wireless communications, 2020

347 citations recorded by the source corpus.

As the 5G standard is being completed, academia and industry have begun to consider a more developed cellular communication technique, 6G, which is expected to achieve high data rates up to 1 Tb/s and broad frequency bands of 100 GHz to 3 THz. Besides the significant upgrade of the key communication metrics, Artificial Intelligence (AI) has been envisioned by many researchers as the most important feature of 6G, since the state-of-the-art machine learning technique has been adopted as the top solution in many extremely complex scenarios. Network intelligentization will be the new trend to address the challenges of exponentially increasing number of connected heterogeneous devices. However, compared with the application of machine learning in other fields, such as computer games, current research on intelligent networking still has a long way to go to realize the automatically- configured cellular communication systems. Various problems in terms of communication system, machine learning architectures, and computation efficiency should be addressed for the full use of this technique in 6G. In this paper, we analyze machine learning techniques and introduce 10 most critical challenges in advancing the intelligent 6G system.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/16691c78b5948b5dd6b4efe42870f47460662c02

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Electrical and Electronic Engineering

Citations in the corpus

Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions (2021, background): “In contrast to existing works [1]–[3] on 6 G, this paper presents a new perspective regarding the development of intelligent, self-sustaining 6 G wireless systems enabled by digital twin.”
A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges (2020, background): “With the development of machine learning, deep learning and artificial intelligence, many learning-based algorithms have been proposed to solve channel estimation [161]–[170], automatic modulation recognition [171]–[175], network traffic prediction [176]–[184], computing offloading [185]–[192], phys”
The Road Towards 6G: A Comprehensive Survey (2021, background): “[27] recognized possible challenges and potential research directions of advancing ML technologies into the future 6G network from the perspectives of communication, networking, and computing.”