Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/120377
Title: Machine Learning Paradigms for Next-Generation Wireless Networks
Authors: Chunxiao Jiang;Haijun Zhang;Yong Ren;Zhu Han;Kwang-Cheng Chen;Lajos Hanzo
Year: 2017
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/120377
volume: 24
issue: 2
More Information: 98,
105
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7792374.pdf193.84 kBAdobe PDF
Title: Machine Learning Paradigms for Next-Generation Wireless Networks
Authors: Chunxiao Jiang;Haijun Zhang;Yong Ren;Zhu Han;Kwang-Cheng Chen;Lajos Hanzo
Year: 2017
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/120377
volume: 24
issue: 2
More Information: 98,
105
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7792374.pdf193.84 kBAdobe PDF
Title: Machine Learning Paradigms for Next-Generation Wireless Networks
Authors: Chunxiao Jiang;Haijun Zhang;Yong Ren;Zhu Han;Kwang-Cheng Chen;Lajos Hanzo
Year: 2017
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/120377
volume: 24
issue: 2
More Information: 98,
105
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7792374.pdf193.84 kBAdobe PDF