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Reduction of the Noise Effect to Detect the DSSS Signal using the Artificial Neural Network

机译:使用人工神经网络减少噪声效应以检测DSSS信号

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This paper presents the design of a direct sequence spread spectrum system. the data bits are transmitted over the Additive White Gaussian Noise (AWGN) channel this makes the receiver in direct sequence spread spectrum DSSS system )not able to retrieve the original bits without noise and to solve this problem, the proposed model using backpropagation (ANN) artificial neural network, ANN is used to reduce the added noise values to data at 10000 bits each bit is extended by the length of PN code (127 bits ), ANN is succeeded in elimination the noise values in the most cases of Signal-to-Noise Ratio (SNR) . MATLAB, being utilized to design DSSS systems for obtaining a system parameter as well as The bit error rate BER performance of the system is evaluated in the AWGN environment at different values of SNR. The proposed method is succeeded in the detection of the data signal at BER equals 0 in the most cases of SNR.
机译:本文介绍了直接序列扩频系统的设计。 数据位通过加性白色高斯噪声(AWGN)信道发送,这使得接收器在直接序列扩频DSSSS系统中)不能检索原始位而没有噪声并解决该问题,所提出的模型使用BackPropagation(ANN) 人工神经网络,ANN用于将附加的噪声值减少到10000位的数据,每个位由PN码的长度延伸(127位),ANN成功消除了最多的信号到的噪声值 - 噪声比(SNR)。 用于设计DSSS系统以获得系统参数的DSSS系统以及SNR的不同值的AWGN环境中评估系统的误码率BER性能。 在最多的SNR的情况下,所提出的方法在检测到BER等于0的数据信号。

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