基于DRAGAN的通信信号波形生成技术
来源:用户上传
作者:冯奇 张君毅 陈丽 刘芳
摘 要: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: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
中图分类号:TN975 文献标识码:A DOI: 10.7535/hbgykj.2022yx01001
Abstract:In order to solve the problem that complex signals with a specific frame structure were difficult to reconstruct in the case of non-cooperative communication,a method of reconstructing signals by using Deep Regret Analytic Generative Adversarial Networks (DRAGAN) was designed.Firstly,no-regret algorithms were used to constrain the loss function of the discriminator,and the gradient of the discriminator was forced to change in a more stable direction.Secondly,through the confrontation learning between the generator and the discriminator,the distribution of the generator was gradually fitted to the potential distribution of the target data.Finally,a complex signal model with a specific frame was constructed,and the experimental verification of DRAGAN method was carried out.The simulation results show that when the signal-to-noise ratio is 9 dB or above,the generated signal not only learns the modulation style,symbol rate and frequency bandwidth of the sample signal,but also accurately restores the symbol information of a specific frame.Compared with the traditional methods,the signal generated by DRAGAN has the characteristics of high correlation,simple reconstruction process and strong generalization ability.The designed network model has practical value in the construction of electromagnetic environment and other scenes.
Keywords:wireless communication technology;signal reconstruction;generative adversarial networks;no-regret algorithm;electromagnetic environment construction
复杂电磁环境构建[1-2]是无线通信领域的一个重要研究方向,尤其当今电磁环境十分复杂,无论是在空间、空中、海上和陆地,所有的通信信号都伴随着日趋复杂多样的人为干扰、无意串扰或者大自然产生的雷暴等信号。为了提高自身通信系统适应未来战场电磁环境能力,各军事强国、区域大国和纠纷地区国家都增强了复杂电磁环境构建的关注度。通信信号生成[3-4]技术是复杂电磁环境构建中的关键一环,对此开展研究具有重要意义。
对于空间中非合作方的通信信号生成,有两种传统解决方式:一种是基于参数测量分析,通过捕获目标信号,对其码速率、调制样式、载频等参数进行估计后,通过信号重构方式完成通信信号的生成;另一种是基于盲侦察、盲干扰的方式,对目标信号进行稀疏采样后,侦知其稀疏特性,再施以信号重构。然而,现代战场上的电磁环境中必然存在大量特殊结构的新型通信信号,凭借传统方式已经无法准确勾画出目标信号特征,亟需一种新型信号生成方式的问世。
随着计算机计算能力的提升,许多基于深度学习[5]的智能化模型和算法被提出,解决了传统通信技术无法解决的大量难题,其中,生成对抗网络(generative adversarial networks,GAN)[6-8]展现出非常强大的能力,GAN是一种隐式的生成模型,可以在不知道目标样本先验知识的情况下,学习样本在空间中的分布,生成符合目标特征的数据。
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