Performance evaluation of Dynamic Neural Networks for mobile radio path loss prediction

A. Bhuvaneshwari, R. Hemalatha, T. Satyasavithri
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引用次数: 10

Abstract

The prediction of path loss for the mobile radio signals is an important part in the design phase of the wireless cellular networks. In the process of modelling the path loss, the GSM 900 MHz signals are collected experimentally using Test Mobile System (TEMS) tool in the dense urban environment of Hyderabad city. In this paper, the best suited Cost 231 Hata empirical propagation model is implemented using three major dynamic neural networks namely, Focused Time Delay Neural Network (FTDNN), Distributed Time Delay Neural Network (DTDNN) which are feed forward dynamic neural networks and Layer Recurrent Neural Network (LRNN) which is a feedback dynamic neural network. The aim of these implementations is to minimise the errors between simulations and measurements. The dynamic neural networks are trained using Levenberg-Marquardt and Scaled Conjugate Gradient training algorithms. Comparisons are made by varying the number of neurons in the hidden layer and changing the training epochs. The performance is analysed in terms of correlation with the measured data, standard deviation, mean error between the targets and outputs and computation times. From the results it is inferred that, the best correlation between simulations and measurements is 0.9972, standard deviation of error (0.04) and mean error (−5.379e-5) are least for Layer Recurrent Neural Network, trained by Levenberg method, but at the cost of increased computation time. With respect to the feed forward dynamic networks, the results show that FTDNN trained by Levenberg algorithm has a better performance compared to DTDNN.
动态神经网络在移动无线电路径损耗预测中的性能评价
移动无线电信号的路径损耗预测是无线蜂窝网络设计阶段的重要组成部分。在模拟路径损耗的过程中,利用测试移动系统(TEMS)工具在海得拉巴市密集的城市环境中对GSM 900 MHz信号进行了实验采集。本文采用三种主要的动态神经网络,即聚焦时延神经网络(FTDNN)、分布式时延神经网络(DTDNN)(前馈动态神经网络)和层递归神经网络(LRNN)(反馈动态神经网络),实现了最适合的Cost 231 Hata经验传播模型。这些实现的目的是尽量减少模拟和测量之间的误差。采用Levenberg-Marquardt和缩放共轭梯度训练算法对动态神经网络进行训练。通过改变隐藏层中神经元的数量和改变训练周期来进行比较。从与测量数据的相关性、标准偏差、目标与输出之间的平均误差和计算次数等方面分析了性能。结果表明,Levenberg方法训练的Layer Recurrent Neural Network的模拟与测量的最佳相关性为0.9972,标准差(0.04)和平均误差(- 5.379e-5)最小,但代价是增加了计算时间。对于前馈动态网络,结果表明Levenberg算法训练的FTDNN比DTDNN具有更好的性能。
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