Abstract
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.
Highlights
With a fast-growing number of mobile devices and sensors, wireless networks (e.g., heterogeneous networks (HetNets), ultra-dense networks, and unmanned aerial vehicle (UAV) networks) become more autonomous, complex and dynamic in nature
We propose a novel method based on deep reinforcement learning for power allocation problem in D2D communication
We present a non-cooperative energy efficient approach based on deep reinforcement learning (DRL)
Summary
With a fast-growing number of mobile devices and sensors, wireless networks (e.g., heterogeneous networks (HetNets), ultra-dense networks, and unmanned aerial vehicle (UAV) networks) become more autonomous, complex and dynamic in nature. It incurs a fast escalation of energy demand and requirements for efficient resource allocation. The critical problem for wireless network energy efficiency is the trade-off between energy consumption and guaranteed performance (e.g., throughput or quality of service (QoS)). In advanced wireless networks with an enormous number of devices, the environment is often dynamically unstable. It is desirable to enable network nodes to have autonomous decision-making ability. The decisions must be based directly on local observations, e.g., power allocation, spectrum access, and interference management, to maximize the network performance
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