Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Tsinghua University · National Engineering Research Center for Information Technology in Agriculture · +3 more institutions
Abstract
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…
Citation impact
- FWCI
- 101.91
- Percentile
- 100%
- References
- 340
Authors
6- JWJingjing WangCorresponding
Tsinghua University
- CJChunxiao Jiang
National Engineering Research Center for Information Technology in Agriculture, Tsinghua University
- HZHaijun Zhang
University of Science and Technology Beijing
- YRYong Ren
Tsinghua University
- KCKwang-Cheng Chen
University of South Florida
Topics & keywords
- Wireless network
- Wireless
- Reinforcement learning
- The Internet
- Cognitive radio
- Big data
- Deep learning
Funding
- RSRoyal Society
- NNNational Natural Science Foundation of ChinaAwards: 61822104, 61771044, 61922050
- CAChina Academy of Space TechnologyAward: Co/Co-20180605-47
- FCFlorida Center for Cybersecurity, University of South Florida
- EAEngineering and Physical Sciences Research CouncilAwards: EP/PO34284/1, EP/Noo4558/1
- FRFundamental Research Funds for the Central UniversitiesAwards: FRF-TP-19-002C1, RC1631