Open AccessApplication of deep neural network and deep reinforcement learning in wireless communication
Author(s) -
Ming Li,
Hui Li
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0235447
Subject(s) - reinforcement learning , computer science , wireless , rate of convergence , cognitive radio , cluster analysis , power control , wireless network , convergence (economics) , artificial neural network , matlab , artificial intelligence , machine learning , power (physics) , computer network , telecommunications , channel (broadcasting) , physics , quantum mechanics , economics , economic growth , operating system
Objective To explore the application of deep neural networks (DNNs) and deep reinforcement learning (DRL) in wireless communication and accelerate the development of the wireless communication industry. Method This study proposes a simple cognitive radio scenario consisting of only one primary user and one secondary user. The secondary user attempts to share spectrum resources with the primary user. An intelligent power algorithm model based on DNNs and DRL is constructed. Then, the MATLAB platform is utilized to simulate the model. Results In the performance analysis of the algorithm model under different strategies, it is found that the second power control strategy is more conservative than the first. In the loss function, the second power control strategy has experienced more iterations than the first. In terms of success rate, the second power control strategy has more iterations than the first. In the average number of transmissions, they show the same changing trend, but the success rate can reach 1. In comparison with the traditional distributed clustering and power control (DCPC) algorithm, it is obvious that the convergence rate of the algorithm in this research is higher. The proposed DQN algorithm based on DRL only needs several steps to achieve convergence, which verifies its effectiveness. Conclusion By applying DNNs and DRL to model algorithms constructed in wireless scenarios, the success rate is higher and the convergence rate is faster, which can provide experimental basis for the improvement of later wireless communication networks.