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

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 i...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.100480-100490
Main Authors: Nguyen, Khoi Khac, Duong, Trung Q., Vien, Ngo Anh, Le-Khac, Nhien-An, Nguyen, Minh-Nghia
Format: Article
Language:English
Subjects:
Communication
D2D communication
Deep learning
deep reinforcement learning
Device-to-device communication
Electronic devices
Energy consumption
Energy efficient wireless communication
Energy sources
Game theory
Games
Interference
Mobile ad hoc networks
multi-agent reinforcement learning
Multiagent systems
Network latency
Optimization
power allocation
Power management
Reinforcement learning
Resource allocation
Resource management
Sensors
Wireless communications
Wireless networks
Wireless sensor networks
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2930115