基于深度卷积神经网络的变换域通信网络抗干扰优化算法
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引用本文:孙桂萍, 唐艳娜 ,于爱华.基于深度卷积神经网络的变换域通信网络抗干扰优化算法[J].计算技术与自动化,2023,(2):119-123
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作者单位
孙桂萍, 唐艳娜 ,于爱华 (青岛农业大学 理学与信息科学学院山东 青岛 266109) 
中文摘要:为了有效抑制变换域通信网络干扰信号,改善信噪比,研究了基于深度卷积神经网络的变换域通信网络抗干扰优化算法。应用傅里叶变换方法将信号从时域转换到频域,并以傅里叶变换通信信号获得的参数为依据构建干扰信号模型;嵌入干扰信号模型以形成接收信号,然后对接收信号进行处理并存储在干扰数据库中,利用深度卷积神经网络完成干扰信号的特征学习与干扰估计,并根据干扰估计结果,在接收信号中去除干扰信号,完成变换域通信网络抗干扰优化。实验结果表明:该算法可有效完成变换域通信网络抗干扰优化,优化后通信信号的信噪比改善性能与误码性能均较佳,输出的通信信号几乎无干扰信号存在。
中文关键词:深度卷积神经网络  变换域  通信网络  抗干扰优化  傅里叶变换
 
Anti-interference Optimization Algorithm of Transform Domain Communication Network Based on Deep Convolution Neural Network
Abstract:In order to effectively suppress the interference signal of variable domain communication network and improve the signal-to-noise ratio, the anti-interference optimization algorithm of variable domain communication network based on deep convolution neural network is studied. The signal is transformed from time domain to frequency domain by Fourier transform method, and the signal interference model is constructed based on the parameters obtained by Fourier transform communication signal; the signal interference model is embedded to form the received signal, and the received signal is processed and stored in the interference database. The deep convolution neural network is used to complete the feature learning and interference estimation of the interference signal. According to the interference estimation results, the interference signal is removed from the received signal to complete the anti-interference optimization of the transform domain communication network. The experimental results show that the algorithm can effectively complete the anti-interference optimization of variable domain communication network. The signal-to-noise ratio and bit error performance of the optimized communication signal are better, and the output communication signal has almost no interference signal.
keywords:deep convolution neural network  transform domain  communication network  anti-interference optimization  Fourier transform
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