Radio Resource Scheduling for 5G NR via Deep Deterministic Policy Gradient
| Autor: | Yen-Cheng Chou, Zheng-Wei Liu, Sheng-Chia Tseng, Chih-Wei Huang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Radio access network
business.industry Computer science Distributed computing Deep learning 05 social sciences 050801 communication & media studies Scheduling (computing) 0508 media and communications 0502 economics and business Reinforcement learning Wireless 050211 marketing Resource management Artificial intelligence Radio resource management business 5G |
| Zdroj: | ICC Workshops |
| DOI: | 10.1109/iccw.2019.8757174 |
| Popis: | The fifth generation (5G) wireless system plays a crucial role to realize future network applications with diverse services requirements. The 3rd Generation Partnership Project (3GPP) proposed 5G New Radio (NR) specifications with significantly greater flexibility on configurations and procedures to facilitate a more efficient and agile radio access network (RAN). At the same time, the complexity of resource management increases, and the advantage of machine learning techniques are worth studying. In this article, we investigate the radio resource scheduling issue in the 5G RAN. Through a modularized deep deterministic policy gradient (DDPG) architecture and specifically defined action as a combination of scheduling algorithms Through specifically defined action as a combination of scheduling algorithms, the proposed method is efficient to train and performing well. Favorable results are observed compared with conventional scheduling algorithms. The proposed architecture applies to other radio resource management problems with similar characteristic. |
| Databáze: | OpenAIRE |
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