Reinforcement Learning-Based Intelligent Decision-Making for Communication Parameters

被引:4
作者
Xie, Xia [1 ]
Dou, Zheng [1 ]
Zhang, Yabin [1 ]
机构
[1] Harbin Engn Univ, Dept Informat & Commun Engn, Harbin, Heilongjiang, Peoples R China
来源
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS | 2022年 / 16卷 / 09期
基金
中国国家自然科学基金;
关键词
reinforcement learning; decision-making; Q-learning; cognitive radio; adaptive modulation and coding; COGNITIVE RADIO; ACCESS;
D O I
10.3837/tiis.2022.09.006
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:2942 / 2960
页数:19
相关论文
共 31 条
[1]   Recent advances on artificial intelligence and learning techniques in cognitive radio networks [J].
Abbas, Nadine ;
Nasser, Youssef ;
El Ahmad, Karim .
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2015, :1-20
[2]  
Amanna A, 2015, 2015 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE FOR SECURITY AND DEFENSE APPLICATIONS (CISDA), P47
[3]  
[Anonymous], 2004, THESIS VIRGINIA TECH
[4]   A Survey on Machine-Learning Techniques in Cognitive Radios [J].
Bkassiny, Mario ;
Li, Yang ;
Jayaweera, Sudharman K. .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2013, 15 (03) :1136-1159
[5]  
Bruno R, 2014, 2014 IEEE 80TH VEHICULAR TECHNOLOGY CONFERENCE (VTC FALL)
[6]  
Chen Y, 2009, 2009 INTERNATIONAL CONFERENCE ON INDUSTRIAL MECHATRONICS AND AUTOMATION, P468, DOI 10.1109/ICIMA.2009.5156664
[7]  
Daniels Robert, 2010, 2010 European Wireless Conference (EW), P718, DOI 10.1109/EW.2010.5483527
[8]  
Dou Z., 2017, PROC INT C ADV HYBRI, P465
[9]  
Ferreira P., 2016, 34 AIAA INT COMM SAT, P5726
[10]  
Ferreira PVR, 2017, 2017 COGNITIVE COMMUNICATIONS FOR AEROSPACE APPLICATIONS WORKSHOP (CCAA)