AIM: Having a reliable prediction model of radio signal strength is an essential tool for planning and designing a radio network. Given a geographic region, and associated power estimates linked to the transmitter placements, our objective is to develop machine learning models to predict the strength of the radio signals. BACKGROUND: The propagation model is often used to determine the optimal location of radio transmitters in order to optimize the power coverage in a geographic area of interest. However, it is often a costly operation to obtain the exact power measurements over a region for a given set of transmitter locations. Therefore, fast prediction methods are needed to estimate the power values given limited data. METHODOLOGY: We consider a dataset consisting of simulated power at each point in an environment for a given set of transmitter locations. We experiment with various machine learning models, namely, generalized linear models (GLMs), neural networks (NNs), and k-nearest neighbor (KNN), to estimate the power values for a given transmitter placement. We investigate various feature engineering approaches to enhance the predictive performance of the machine learning models. RESULTS: We observe that employed feature engineering methods such as polynomial degrees and transmitter to cluster distances significantly improve the prediction accuracy. In particular, GLM model performance notably improves thanks to these extracted features, where mean absolute error (MAE) is reduced around 77% from 11.37 dB to 2.55 dB. We note that KNN with k = 2 and DNN models perform better than NN and GLM. KNN has the best performance with an average MAE of 0.65dB and also substantially faster to train than NN/DNN models. In addition, our analysis shows that, to train a well-performing machine learning model, it is sufficient to use a dataset consisting of measurements at a fraction of the potential transmitter locations in a given region. CONCLUSIONS: Machine learning methods are highly effective for the coverage prediction task. Using carefully engineered features, simple models such as GLMs and KNNs can be as effective as more complex ones, especially for small datasets.

中文翻译：

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城市环境中基于机器学习的无线电覆盖预测


目的：拥有可靠的无线电信号强度预测模型是规划和设计无线电网络的重要工具。给定地理区域以及与发射器放置相关的相关功率估计，我们的目标是开发机器学习模型来预测无线电信号的强度。背景：传播模型通常用于确定无线电发射机的最佳位置，以优化感兴趣地理区域的功率覆盖范围。然而，对于给定的一组发射机位置，获取某个区域的精确功率测量结果通常是一项成本高昂的操作。因此，需要快速预测方法来估计给定有限数据的功率值。方法：我们考虑一个数据集，其中包含给定一组发射机位置的环境中每个点的模拟功率。我们尝试了各种机器学习模型，即广义线性模型 (GLM)、神经网络 (NN) 和 k 最近邻 (KNN)，以估计给定发射机放置的功率值。我们研究了各种特征工程方法来增强机器学习模型的预测性能。结果：我们观察到，采用多项式度和发射器到簇距离等特征工程方法显着提高了预测精度。特别是，由于这些提取的特征，GLM 模型性能显着提高，平均绝对误差 (MAE) 从 11.37 dB 降低到 2.55 dB 约 77%。我们注意到 k = 2 的 KNN 和 DNN 模型的性能优于 NN 和 GLM。 KNN 具有最佳性能，平均 MAE 为 0.65dB，并且训练速度也比 NN/DNN 模型快得多。 此外，我们的分析表明，要训练性能良好的机器学习模型，使用由给定区域中一小部分潜在发射器位置的测量组成的数据集就足够了。结论：机器学习方法对于覆盖率预测任务非常有效。使用精心设计的特征，GLM 和 KNN 等简单模型可以与更复杂的模型一样有效，尤其是对于小型数据集。