A Machine Learning Based 3D Propagation Model for Intelligent Future Cellular Networks
A Machine Learning Based 3D Propagation Model for Intelligent Future Cellular Networks
复制标题
基于机器学习的智能未来蜂窝网络 3D 传播模型
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
A. Imran
中科院分区：
文献类型：
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作者：
Usama Masood;H. Farooq;A. Imran
在现代无线通信系统中，电波传播建模一直是系统设计和性能优化的一项基本任务。这些模型用于蜂窝网络和其他无线电系统中，以估计接收器处的路径损耗或接收信号强度（RSS），或表征信号穿过的环境。准确和灵活的路径损耗估计是实现所需优化目标的必要条件。最先进的经验传播模型是基于特定环境中的测量，并且其捕获各种传播环境的特性的能力有限。为了科普这个问题，商业规划工具中使用了基于射线跟踪的解决方案，但它们往往非常耗时且昂贵。在本文中，我们提出了一种基于机器学习（ML）的方法，以补充经验或射线跟踪为基础的模型，无线电波传播建模和RSS估计。建议ML为基础的模型利用一组预先确定的智能预测，包括发射机参数和物理和几何特性的传播环境，估计RSS。这些智能预测器在网络侧很容易获得，不需要进一步标准化。我们已经定量地比较了几种机器学习算法在捕获信道特征的能力方面的性能，即使在训练数据稀疏的情况下也是如此。我们的研究结果表明，深度神经网络的性能优于其他ML技术，与最先进的经验模型相比，预测精度提高了25%，与光线跟踪相比，预测时间缩短了12倍。
In modern wireless communication systems, radio propagation modeling has always been a fundamental task in system design and performance optimization. These models are used in cellular networks and other radio systems to estimate the pathloss or the received signal strength (RSS) at the receiver or characterize the environment traversed by the signal. An accurate and agile estimation of pathloss is imperative for achieving desired optimization objectives. The state-of-the- art empirical propagation models are based on measurements in a specific environment and limited in their ability to capture idiosyncrasies of various propagation environments. To cope with this problem, ray-tracing based solutions are used in commercial planning tools, but they tend to be extremely time consuming and expensive. In this paper, we propose a Machine Learning (ML) based approach to complement the empirical or ray tracing-based models, for radio wave propagation modeling and RSS estimation. The proposed ML-based model leverages a pre-identified set of smart predictors, including transmitter parameters and the physical and geometric characteristics of the propagation environment, for estimating the RSS. These smart predictors are readily available at the network-side and need no further standardization. We have quantitatively compared the performance of several machine learning algorithms in their ability to capture the channel characteristics, even with sparse availability of training data. Our results show that Deep Neural Networks outperforms other ML techniques and provides a 25% increase in prediction accuracy as compared to state-of-the-art empirical models and a 12x decrease in prediction time as compared to ray tracing.