Power Control Based on Multi-Agent Deep Q Network for D2D Communication
| Autor: | Shigeru Shimamoto, Megumi Saito, Liu Jiang, Pan Zhenni, Shi Gengtian, Takashi Koshimizu |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science Distributed computing Quality of service Throughput 02 engineering and technology Interference (wave propagation) Term (time) Shared resource 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Reinforcement learning 020201 artificial intelligence & image processing Power control |
| Zdroj: | ICAIIC |
| Popis: | In device-to-device (D2D) communication under a cell with resource sharing mode the spectrum resource utilization of the system will be improved. However, if the interference generated by the D2D user is not controlled, the performance of the entire system and the quality of service (QOS) of the cellular user may be degraded. Power control is important because it helps to reduce interference in the system. In this paper, we propose a reinforcement learning algorithm for adaptive power control that helps reduce interference to increase system throughput. Simulation results show the proposed algorithm has better performance than traditional algorithm in LTE (Long Term Evolution). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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