基于神经网络的扩频PN码采集系统

S. El-Khamy, E. Gelenbe, H. Abdelbaki
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引用次数: 3

摘要

提出了一种基于神经网络的直接序列扩频码同步系统。该系统是基于对所使用的扩频码的所有可能阶段训练一个循环随机神经网络(RNN)模型。然后,训练好的网络可以在接收器上用于本地代码阶段和接收到的代码的初始粗对齐。与传统的同步技术相比,该技术的一个优点是可以在不搜索潜在码相位的情况下确定接收到的PN码的相位。此外,经过训练的RNN可以有一个简单的硬件实现,使其成为专用芯片的候选实现。这使得基于神经网络的技术比传统技术更快、更健壮。在长度为N=7和N=15的最大长度序列上进行的计算机模拟表明,即使在非常低的信噪比下,所提出的系统也能有效地指示接收码的相位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural network based spread spectrum PN code acquisition system
A neural network based direct sequence spread spectrum code synchronization system is proposed. The's system is based on training a recurrent random neural network (RNN) model on all the possible phases of the used spreading code. The trained network can then be used at the receiver for the initial coarse alignment of the local code phase and the received code. One advantage of this technique over the conventional synchronization techniques is that the phase of the received PN code can be decided without searching the potential code phases. Also the RNN, after being trained, can have a simple hardware realization that makes it candidate for implementation as a dedicated chip. This makes the neural network based technique faster and more robust than the conventional techniques. Computer simulations, carried out on maximal length sequences of length N=7 and N=15, show that the proposed system cast effectively indicate the phase of the received code even with very low signal to noise ratios.
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