Deep Learning Based Energy Efficiency Optimization for Distributed Cooperative Spectrum Sensing

Deep learning has achieved remarkable breakthroughs in the past decade across a wide range of application domains, such as computer games, natural language processing, pattern recognition, and medical diagnosis, to name a few. In this article, we investigate the application of deep learning techniqu...

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Vydané v:IEEE wireless communications Ročník 26; číslo 3; s. 32 - 39
Hlavní autori: He, Haibo, Jiang, He
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Artificial intelligence
Cognitive radio
Cognitive systems
Combinatorial analysis
Computer & video games
Computer simulation
Deep learning
Detection
Domains
Energy efficiency
Graph neural networks
Machine learning
Natural language processing
Optimization
Pattern recognition
Power efficiency
Protocols
Sensors
Signal to noise ratio
Wireless communication systems
Wireless communications
ISSN:1536-1284, 1558-0687
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Shrnutí:Deep learning has achieved remarkable breakthroughs in the past decade across a wide range of application domains, such as computer games, natural language processing, pattern recognition, and medical diagnosis, to name a few. In this article, we investigate the application of deep learning techniques for wireless communication systems with a focus on energy efficiency optimization for distributed cooperative spectrum sensing. With the continuous development of today's technologies and user demands, wireless communication systems have become larger and more complex than ever, which introduces many critical challenges that the traditional approaches can no longer handle. We envision that deep learning based approaches will play a pivotal role in addressing many such challenges in the next-generation wireless communication systems. In this article, we focus on cognitive radio, a promising technology to improve spectrum efficiency, and develop deep learning techniques to optimize its spectrum sensing process. Specifically, we investigate the energy efficiency of distributed cooperative sensing by formulating it as a combinatorial optimization problem. Based on this formulation, we develop a deep learning framework by integrating graph neural network and reinforcement learning to improve the overall system energy efficiency. Simulation studies under different network scales demonstrate the effectiveness of our proposed approach.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1536-1284
1558-0687
DOI:10.1109/MWC.2019.1800397