A Cognitive Relay Network Throughput Optimization Algorithm Based on Deep Reinforcement Learning
| Autor: | Ni Weichuan, Liu Shaojiang, Wang Feng, Kejing Hu, Zhiming Xu, Wan Zhiping |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Article Subject
Computer Networks and Communications Computer science Throughput 02 engineering and technology lcsh:Technology law.invention lcsh:Telecommunication 0203 mechanical engineering Relay law lcsh:TK5101-6720 0202 electrical engineering electronic engineering information engineering Computer Science::Networking and Internet Architecture Reinforcement learning Electrical and Electronic Engineering Computer Science::Information Theory business.industry Wireless network lcsh:T Node (networking) 020302 automobile design & engineering 020206 networking & telecommunications Markov decision process business Information Systems Communication channel Computer network Efficient energy use |
| Zdroj: | Wireless Communications and Mobile Computing, Vol 2019 (2019) |
| ISSN: | 1530-8669 |
| DOI: | 10.1155/2019/2731485 |
| Popis: | In cognitive relay networks, the cognitive user opportunistically accesses the authorized spectrum segment of the primary user and simultaneously serves as the data relay node of the primary user while sharing the spectrum resource of the primary user. This not only improves the utilization efficiency of the network spectrum resources but also improves the throughput of the primary users. However, if the primary user randomly selects the relay node, there is no guarantee for an optimal throughput. Moreover, the system power consumption may increase. In order to improve the throughput of cognitive relay network and optimize system utility, this paper proposes a cognitive relay network throughput optimization algorithm based on deep reinforcement learning. For the system model of cognitive relay networks, the Markov decision process is used to describe the channel transition probability of the system model in the paper. The algorithm proposes a cooperative wireless network cooperative relay strategy, analyzes the system outage probability under different transmission modes, and optimizes the system throughput by minimizing the outage probability. Then, the maximum utility optimization strategy based on deep reinforcement learning is proposed to maximize the system utility revenue by selecting the optimal behavior. The experimental results show that the proposed algorithm has a good effect in improving system throughput and optimizing system energy efficiency. |
| Databáze: | OpenAIRE |
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