基于人工神经网络的微蜂窝覆盖预测

A. Neskovic, N. Neskovic, D. Paunovic
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引用次数: 4

摘要

提出了一种新的手机环境微蜂窝预测模型。该模型基于流行的前馈神经网络原理。利用新的人工神经网络模型可以克服确定性模型和经验模型的一些重要缺点。为了建立该模型,在贝尔格莱德市对两个不同的测试发射机位置进行了广泛的电场电平测量(900 MHz频段)。将所建立的电场能级预测模型与独立测量集的实测数据进行比较,结果表明所建立的模型具有较高的精度(在局部平均测量不确定度的量级上)和可靠性。同时,该算法适合在计算机上实现,简单快捷。
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Microcell coverage prediction using artificial neural networks
A new microcell prediction model for mobile phone environment is presented in this paper. The model is based on the principles of popular feedforward neural networks. Utilising a new artificial neural network model some important disadvantages of both deterministic and empirical models can be overcome. In order to build the model, extensive electric field level measurements (in 900 MHz frequency band) were carried out in the city of Belgrade, for two different test transmitter locations. The comparison between the data obtained by the proposed electric field level prediction model and the independent measurement sets, have shown that the proposed model is accurate (on the order of the local mean measurements uncertainty) and reliable. At the same time, the algorithm is suitable for computer implementation, simple and fast.
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