APSIPA Transactions on Signal and Information Processing > Vol 11 > Issue 1

Machine Learning for Wireless Communication: An Overview

Zijian Cao, The National Mobile Communications Research Laboratory, Southeast University, China, Hua Zhang, The National Mobile Communications Research Laboratory, Southeast University, China, huazhang@seu.edu.cn , Le Liang, The National Mobile Communications Research Laboratory, Southeast University, China, Geoffrey Ye Li, The ITP Lab, Department of Electrical and Electronic Engineering, Imperial College London, UK
 
Suggested Citation
Zijian Cao, Hua Zhang, Le Liang and Geoffrey Ye Li (2022), "Machine Learning for Wireless Communication: An Overview", APSIPA Transactions on Signal and Information Processing: Vol. 11: No. 1, e33. http://dx.doi.org/10.1561/116.00000029

Publication Date: 31 Oct 2022
© 2022 Z. Cao, H. Zhang, L. Liang and G. Y. Li
 
Subjects
 
Keywords
Machine learningsignal processingend-to-end communicationresource allocationfederated learning
 

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This is published under the terms of CC BY-NC.

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In this article:
Introduction 
ML Driven Signal Processing 
End-to-End and Semantic Communications 
ML Based Resource Allocation 
Federated Learning for Distributed Systems 
Selected Topics for ML Wireless Communications 
Open Challenges and Opportunities 
Conclusion 
References 

Abstract

Over the past decades, machine learning techniques have demonstrated excellent superiorities in a wide range of fields, such as computer vision, natural language processing, etc. Through efficient utilization of a huge amount of data, machine learning techniques can solve problems that are hard or impossible for conventional model-based solutions, because the simplified models cannot effectively approximate actual scenarios while complicated models cannot be practically solved in a mathematically rigorous sense. In the meantime, future wireless communication systems are becoming increasingly complex due to diverse practical demands and communication applications. This makes it urgent to find alternatives to conventional solutions and warrants a paradigm shift towards the machine learning-driven direction. Although the convergence of wireless communication and machine learning is just unfolding, it has already achieved initial success in academic research and practical applications. This paper reviews the latest research of machine learning in wireless communications. We highlight key technologies of machine learning-driven signal processing, end-to-end communications and semantic communications, machine learning-based resource allocation, and federated learning of distributed systems. Furthermore, open challenges and potential opportunities in the convergence of machine learning and wireless communication are also illustrated.

DOI:10.1561/116.00000029