Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments,International Journal of Communication Systems

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Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2020-11-18 , DOI: 10.1002/dac.4680
Stephen Ojo ₁ , Agbotiname Imoize ₂ , Daniel Alienyi ₃

Affiliation

Path loss prediction models occupy a central role in wireless signal propagation because of the continuous need to achieve reliable and high quality of service for subscribers satisfaction. However, the adoption of deterministic and empirical models for pathloss characterization presents a highly contending trade‐off between simplicity and accuracy. On the one hand, empirical models are relatively simple to apply but are mostly inaccurate and inconsistent. Deterministic models are more accurate but quite complex to develop, time‐consuming, and possess nonadaptable characteristics. Toward this end, this paper proposes to address the problems associated with the existing models (empirical and deterministic) through the introduction of machine learning algorithms to path loss predictions. The contribution of this paper is in threefold. First, experimental data were collected in multitransmitter scenarios via drive test in six base transceiver stations, and the pathloss of the received signal level was derived and analyzed. Two machine learning‐based path loss prediction models were then developed using the measured data as input variables. The developed path loss prediction models are the radial basis function neural network (RBFNN) and the multilayer perception neural network (MLPNN). Further to this, the MLPNN and the RBFNN models were compared with the measured path loss, and the RBFNN appears to be more accurate with lower values of root mean squared errors (RMSEs) in comparison with the MLPNN. Finally, the proposed machine language‐based path loss prediction models (MLPNN and RBFNN) were compared against five existing empirical models, and again, the RBFNN shows the most accurate results.

中文翻译：

多发射机信号传播环境下LTE网络的径向基函数神经网络路径损耗预测模型

路径损耗预测模型在无线信号传播中起着核心作用，因为持续需要获得可靠和高质量的服务以使用户满意。但是，采用确定性和经验模型进行路径损耗特性描述在简单性和准确性之间存在着激烈的权衡。一方面，经验模型的应用相对简单，但大多不准确且不一致。确定性模型更准确，但开发却十分复杂，耗时且具有不可适应的特征。为此，本文建议通过将机器学习算法引入路径损耗预测来解决与现有模型（经验和确定性）相关的问题。本文的贡献是三方面的。第一，通过在六个基站收发器中进行的路测，在多发射器场景中收集了实验数据，并推导并分析了接收信号电平的路径损耗。然后，使用测得的数据作为输入变量，开发了两个基于机器学习的路径损耗预测模型。所开发的路径损耗预测模型是径向基函数神经网络（RBFNN）和多层感知神经网络（MLPNN）。此外，将MLPNN和RBFNN模型与测得的路径损耗进行了比较，并且与MLPNN相比，RBFNN似乎更精确，且均方根误差（RMSE）较低。最后，将提出的基于机器语言的路径损耗预测模型（MLPNN和RBFNN）与五个现有的经验模型进行了比较，再次，

更新日期：2021-01-04

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