Anti-strong Jamming Polar Coding Optimization Method with Multiobjective Reinforcement Learning

被引：0
|
作者：
Liang H. [1 ]
Ye G. [1 ]
Lu R. [1 ]
Wang H. [1 ]
Wei P. [1 ]
机构：
[1] Sixty-third Research Institute, National University of Defense Technology, Nanjing
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2023年 / 45卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Anti-jamming; Channel coding; Polar codes; Reinforcement learning; Reliability performance;
D O I：
10.11999/JEIT230572
中图分类号：
学科分类号：
摘要：
In order to improve the reliability and anti-jamming ability of information transmission for the Frequency-Hopping (FH) communication system, a Polar coding construction optimization method is proposed to adapt to the strong-jamming environment, which is based on a novel Polar coded slow FH communication system model. Firstly, the multi-objective reinforcement learning algorithm is designed for the hybrid channel containing normal state and jamming state, and then the information bit-channel sequence in the coding process is optimized. Consequently the error correction performance of the designed Polar codewords is improved. In addition, the complexity of algorithm is reduced by preprocessing the initialization and theoretically calculating the reward values. The simulation results show that the overall error performance of the proposed coding optimization method is better than those of conventional coding construction methods in the hybrid channel containing strong jamming. Compared with the 3rd Generation Partnership Project (3GPP) standard scheme in Fifth-Generation (5G) mobile communication systems, the obtained overall coding gain is up to 0.5 dB. Therefore the high-reliability and anti-jamming performance of Polar coded FH transmission is effectively improved. © 2023 Science Press. All rights reserved.
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页码：4092 / 4100
页数：8
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