期刊
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
卷 22, 期 3, 页码 1472-1514出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/COMST.2020.2965856
关键词
Wireless networks; Wireless sensor networks; Internet of Things; Ad hoc networks; Maximum likelihood estimation; Clustering algorithms; Machine learning (ML); future wireless network; deep learning; regression; classification; clustering; network association; resource allocation
资金
- National Natural Science Foundation of China [61822104, 61771044, 61922050]
- Pre-research Fund of Equipments of Ministry of Education of China [6141A02022615]
- Research Fund of China Academy of Space Technology [Co/Co-20180605-47]
- Fundamental Research Funds for the Central Universities [RC1631, FRF-TP-19-002C1]
- Shuimu Tsinghua Scholar Program
- Cyber Florida
- Engineering and Physical Sciences Research Council [EP/Noo4558/1, EP/PO34284/1]
- COALESCE, of the Royal Society's Global Challenges Research Fund
- European Research Council
Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of Things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.
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