基于DRAGAN的通信信号波形生成技术Waveform generation technology of communication signal based on DRAGAN
冯奇,张君毅,陈丽,刘芳
摘要(Abstract)：
为了解决非合作通信情况下,具有特定帧结构的复杂信号难以重构问题,设计了一种利用深度无悔分析生成对抗网络(deep regret analytic generative adversarial networks, DRAGAN)重构信号的方法。首先利用无悔算法(no-regret algorithms)对判别器损失函数进行约束,判别器的梯度被迫向更加稳定的方向变化;其次通过生成器与判别器的对抗学习,生成器的分布逐步拟合到目标数据的潜在分布;最后构建具有特定帧的复杂信号模型,并据此进行DRAGAN方法的实验验证。仿真实验结果表明,在信噪比为9 dB及以上的条件下,生成信号不仅学习到了样本信号的调制样式、符号速率和频率带宽等特性,还能较准确还原出特定帧部分的符号信息。相较于传统方法,利用DRAGAN生成信号具有相关性高、重构流程简易和泛化能力强等特点,所设计的网络模型在电磁环境构建等场景中具有实用价值。
关键词(KeyWords)： 无线通信技术;信号重构;生成对抗网络;无悔算法;电磁环境构建
基金项目(Foundation):
作者(Author): 冯奇,张君毅,陈丽,刘芳
参考文献(References)：
- [1] 周波，申绪涧，戴幻尧，等.基于逼真度的战场电磁环境构建要素分析[J].电子信息对抗技术，2014,29(5):17-20.ZHOU Bo,SHEN Xujian,DAI Huanyao,et al.Analysis of battlefield electromagnetic environment constructing element based on fidelity[J].Electronic Warfare Technology,2014,29(5):17-20.
- [2] WEN Qi.An overview of the study of the complexity of the complex electromagnetic environments[C]//2015 8th International Symposium on Computational Intelligence and Design (ISCID).Hangzhou:IEEE,2015:245-250.
- [3] 李楠，王标，底明曦，等.海战场复杂电磁环境构建研究[J].舰船电子对抗，2021,44(2):11-17.LI Nan,WANG Biao,DI Mingxi,et al.Research into the construction of complex electromagnetic environment for sea battlefield[J].Shipboard Electronic Countermeasure,2021,44(2):11-17.
- [4] 孙翱.复杂电磁环境下武器装备试验与评估[J].舰船电子工程，2017,37(4):81-85.SUN Ao.Test and evaluation on the armament based on complex electromagnetic environment[J].Ship Electronic Engineering,2017,37(4):81-85.
- [5] NIRANJANI V,SELVAM N S.Overview on deep neural networks:Architecture,application and rising analysis trends[C]//Business Intelligence for Enterprise Internet of Things.Cham:Springer,2020:271-278.
- [6] GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial nets[C]//Proceedings of the 27th International Conference on Advances in Neural Information Processing Systems.Boston:MIT Press,2014:2672-2680.
- [7] ALQAHTANI H,KAVAKLI-THORNE M,KUMAR G.Applications of generative adversarial aetworks(GANs):An updated review[J].Archives of Computational Methods in Engineering,2021,28(2):525-552.
- [8] 奚祥品，陈筱，朱向冰.生成对抗网络介绍及应用[J].无线电通信技术，2021,47(3):338-343.XI Xiangpin,CHEN Xiao,ZHU Xiangbing.Introduction and application of generative adversarial networks[J].Radio Communications Technology,2021,47(3):338-343.
- [9] 秦剑.基于生成对抗网络的信号重构[D].西安：西安电子科技大学，2018.QIN Jian.Signal Reconstruction Based on Generative Adversarial Networks[D].Xi′an:Xidian University,2018.
- [10] 杨鸿杰，陈丽，张君毅.基于生成对抗网络的数字信号生成技术研究[J].电子测量技术，2020,43(20):127-132.YANG Hongjie,CHEN Li,ZHANG Junyi.Research on digital signal generation technology based on generative adversarial network[J].Electronic Measurement Technology,2020,43(20):127-132.
- [11] SHI Yi,DAVASLIOGLU K,SAGDUYU Y E.Generative adversarial network for wireless signal spoofing[C]//ACM Workshop on Wireless Security and Machine Learning.New York:Association for Computing Machinery,2019:55-60.
- [12] 赵凡，金虎.基于GAN的通信干扰波形生成技术[J].系统工程与电子技术，2021,43(4):1080-1088.ZHAO Fan,JIN Hu.Communication jamming waveform generation technology based on GAN[J].Systems Engineering and Electronics,2021,43(4):1080-1088.
- [13] KODALI N,ABERNETHY J,HAYS J,et al.On convergence and stability of GANs[EB/OL].(2017-12-10).https://arxiv.org/abs/1705.07215v5.
- [14] ARJOVSKY M,CHINTALA S,BOTTOU L.Wasserstein generative adversarial networks[C]//Proceedings of the 34th International Conference on Machine Learning.Sydney:MLResearch Press,2017:214-223.
- [15] CHEN Xi,DUAN Yan,HOUTHOOFT R,et al.Infogan:Interpretable representation learning by information maximizing generative adversarial Nets[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems.Red Hook:Curran Associates Inc.,2016:2180-2188.
- [16] CHAVDAROVA T,FLEURET F.SGAN:An alternative training of generative adversarial networks[C]//Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition.New York:IEEE.2018:9407-9415.
- [17] WU Jiqing,HUANG Zhiwu,THOMA J,et al.Wasserstein divergence for GANs[C]//Proceedings of the European Conference on Computer Vision.Cham:Springer,2018:673-688.
- [18] NOWOZIN S,CSEKE B,TOMIOKA R.F-gan:Training generative neural samplers using variational divergence minimization[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems.Red Hook:Curran Associates Inc.,2016:271-279.