TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Reinforcement Learning-Based Intelligent Decision-Making for Communication Parameters |
| 영문제목(English Title) |
Reinforcement Learning-Based Intelligent Decision-Making for Communication Parameters |
| 저자(Author) |
Peng Cui
Xuan Luo
Jing Liu
Xia. Xie
Zheng Dou
Yabin Zhang
|
| 원문수록처(Citation) |
VOL 16 NO. 09 PP. 2942 ~ 2960 (2022. 09) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
The core of cognitive radio is the problem concerning intelligent decision-making for communication parameters, the objective of which is to find the most appropriate parameter configuration to optimize transmission performance. The current algorithms have the disadvantages of high dependence on prior knowledge, large amount of calculation, and high complexity. We propose a new decision-making model by making full use of the interactivity of reinforcement learning (RL) and applying the Q-learning algorithm. By simplifying the decision-making process, we avoid large-scale RL, reduce complexity and improve timeliness. The proposed model is able to find the optimal waveform parameter configuration for the communication system in complex channels without prior knowledge. Moreover, this model is more flexible than previous decision-making models. The simulation results demonstrate the effectiveness of our model. The model not only exhibits better decision-making performance in the AWGN channels than the traditional method, but also make reasonable decisions in the fading channels. |
| 키워드(Keyword) |
out-of-distribution
convolutional neural network
mutually exclusive events
fusion networks
autoencoder
reinforcement learning
decision-making
Q-learning
cognitive radio
adaptive modulation
coding
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| 파일첨부 |
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