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Machine learning paradigms for next-generation wireless networks

Machine learning paradigms for next-generation wireless networks
Machine learning paradigms for next-generation wireless networks
Next-generation wireless networks are expected to support extremely high data rates and radically new applications, which require a new wireless radio technology paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and decision making, so that the diverse requirements of next-generation wireless networks can be satisfied. Machine learning is one of the most promising artificial intelligence tools, conceived to support smart radio terminals. Future smart 5G mobile terminals are expected to autonomously access the most meritorious spectral bands with the aid of sophisticated spectral efficiency learning and inference, in order to control the transmission power, while relying on energy efficiency learning/inference and simultaneously adjusting the transmission protocols with the aid of quality of service learning/inference. Hence we briefly review the rudimentary concepts of machine learning and propose their employment in the compelling applications of 5G networks, including cognitive radios, massive MIMOs, femto/small cells, heterogeneous networks, smart grid, energy harvesting, device-todevice communications, and so on. Our goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services.
1536-1284
1-8
Jiang, Chunxiao
16bad068-43b1-41d4-9f6b-211acdb1ae52
Zhang, Haijun
d71f4ebb-34ef-4c27-a2d9-0c9cda44c457
Ren, Yong
ad146a10-75d8-401c-911b-fd4dcc44eb12
Han, Zhu
28e29deb-d470-4165-b198-0923aeac3689
Chen, Kwang-Cheng
537a9ce6-4f1f-4f75-9788-dbcc6a39ec66
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Jiang, Chunxiao
16bad068-43b1-41d4-9f6b-211acdb1ae52
Zhang, Haijun
d71f4ebb-34ef-4c27-a2d9-0c9cda44c457
Ren, Yong
ad146a10-75d8-401c-911b-fd4dcc44eb12
Han, Zhu
28e29deb-d470-4165-b198-0923aeac3689
Chen, Kwang-Cheng
537a9ce6-4f1f-4f75-9788-dbcc6a39ec66
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Jiang, Chunxiao, Zhang, Haijun, Ren, Yong, Han, Zhu, Chen, Kwang-Cheng and Hanzo, Lajos (2016) Machine learning paradigms for next-generation wireless networks. IEEE Wireless Communications, 1-8. (doi:10.1109/MWC.2016.1500356WC).

Record type: Article

Abstract

Next-generation wireless networks are expected to support extremely high data rates and radically new applications, which require a new wireless radio technology paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and decision making, so that the diverse requirements of next-generation wireless networks can be satisfied. Machine learning is one of the most promising artificial intelligence tools, conceived to support smart radio terminals. Future smart 5G mobile terminals are expected to autonomously access the most meritorious spectral bands with the aid of sophisticated spectral efficiency learning and inference, in order to control the transmission power, while relying on energy efficiency learning/inference and simultaneously adjusting the transmission protocols with the aid of quality of service learning/inference. Hence we briefly review the rudimentary concepts of machine learning and propose their employment in the compelling applications of 5G networks, including cognitive radios, massive MIMOs, femto/small cells, heterogeneous networks, smart grid, energy harvesting, device-todevice communications, and so on. Our goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services.

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e-pub ahead of print date: 20 December 2016

Identifiers

Local EPrints ID: 403861
URI: http://eprints.soton.ac.uk/id/eprint/403861
ISSN: 1536-1284
PURE UUID: 9b0b7e3b-f6bb-4c2f-9494-e372651d6650
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 03 Jan 2017 16:32
Last modified: 18 Mar 2024 02:35

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Contributors

Author: Chunxiao Jiang
Author: Haijun Zhang
Author: Yong Ren
Author: Zhu Han
Author: Kwang-Cheng Chen
Author: Lajos Hanzo ORCID iD

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