Non-Cooperative Energy Efficient Power Allocation Game in D2D Communication: A Multi-Agent Deep Reinforcement Learning Approach

Autor: Nhien-An Le-Khac, Minh-Nghia Nguyen, Trung Q. Duong, Khoi Khac Nguyen, Ngo Anh Vien
Rok vydání: 2019
Předmět:
General Computer Science
multi-agent reinforcement learning
Computer science
Wireless ad hoc network
Distributed computing
Power allocation
02 engineering and technology
Materials Science(all)
0203 mechanical engineering
Energy efficient wireless communication
0202 electrical engineering
electronic engineering
information engineering
Reinforcement learning
Wireless
General Materials Science
Resource management
Network performance
Engineering(all)
Deep reinforcement learning
deep reinforcement learning
Wireless network
business.industry
Quality of service
General Engineering
020206 networking & telecommunications
020302 automobile design & engineering
Energy consumption
power allocation
Multi-agent reinforcement learning
D2D communication
Resource allocation
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
Wireless sensor network
Computer Science(all)
Efficient energy use
Zdroj: IEEE Access, Vol 7, Pp 100480-100490 (2019)
Nguyen, K K, Duong, T Q, Vien, N A, Le-Khac, N A & Nguyen, M-N 2019, ' Non-Cooperative Energy Efficient Power Allocation Game in D2D Communication: A Multi-Agent Deep Reinforcement Learning Approach ', IEEE Access, vol. 7, pp. 100480-100490 . https://doi.org/10.1109/ACCESS.2019.2930115
ISSN: 2169-3536
Popis: Recently, there is the widespread use of mobile devices and sensors, and rapid emergence of new wireless and networking technologies, such as wireless sensor network, device-to-device (D2D) communication, and vehicular ad hoc networks. These networks are expected to achieve a considerable increase in data rates, coverage, and the number of connected devices with a significant reduction in latency and energy consumption. Because there are energy resource constraints in user’s devices and sensors, the problem of wireless network resource allocation becomes much more challenging. This leads to the call for more advanced techniques in order to achieve a tradeoff between energy consumption and network performance. In this paper, we propose to use reinforcement learning, an efficient simulation-based optimization framework, to tackle this problem so that user experience is maximized. Our main contribution is to propose a novel non-cooperative and real-time approach based on deep reinforcement learning to deal with the energy-efficient power allocation problem while still satisfying the quality of service constraints in D2D communication.
Databáze: OpenAIRE
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