Power Optimization in Device-to-Device Communications: A Deep Reinforcement Learning Approach With Dynamic Reward
| Autor: | Adnan K. Kiani, Zelin Ji, Zhijin Qin, Rizwan Ahmad |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Distributed computing Quality of service 020302 automobile design & engineering 020206 networking & telecommunications Throughput 02 engineering and technology Power optimization 0203 mechanical engineering Control and Systems Engineering Dynamic demand 0202 electrical engineering electronic engineering information engineering Cellular network Reinforcement learning Resource management Electrical and Electronic Engineering Underlay |
| Zdroj: | IEEE Wireless Communications Letters. 10:508-511 |
| ISSN: | 2162-2345 2162-2337 |
| Popis: | Device-to-Device (D2D) communication can be used to improve system capacity and energy efficiency (EE) in cellular networks. One of the critical challenges in D2D communications is to extend network lifetime by efficient and effective resource management. Deep reinforcement learning (RL) provides a promising solution for resource management in wireless communication systems. This letter aims to maximise the EE while satisfying the system throughput constraints as well as the quality of service (QoS) requirements of D2D pairs and cellular users in an underlay D2D communication network. To achieve this, a deep RL based dynamic power optimization algorithm with dynamic rewards is proposed. Moreover, a novel algorithm with two parallel deep Q networks (DQNs) is designed to maximize the EE of the considered network. The proposed deep RL based power optimization method with dynamic rewards achieves higher EE while satisfying the system throughput requirements. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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