In the last decade, the transition of digital terrestrial television (DTT) systems from multi-frequency networks (MFNs) to single-frequency networks (SFNs) has become a reality. SFN offers multiple advantages concerning MFN, such as more efficient management of the radioelectric spectrum, homogenizing the network parameters, and a potential SFN gain. However, the transition process can be cumbersome for operators due to the multiple measurement campaigns and required finetuning of the final SFN system to ensure the desired quality of service. To avoid time-consuming field measurements and reduce the costs associated with the SFN implementation, this paper aims to predict the performance of an SFN system from the legacy MFN and position data through machine learning (ML) algorithms. It is proposed a ML concatenated structure based on classification and regression to predict SFN electric-field strength, modulation error ratio, and gain. The model’s training and test process are performed with a dataset from an SFN/MFN trial in Ghent, Belgium. Multiple algorithms have been tuned and compared to extract the data patterns and select the most accurate algorithms. The best performance to predict the SFN electric-field strength is obtained with a coefficient of determination (R 2 ) of 0.93, modulation error ratio of 0.98, and SFN gain of 0.89 starting from MFN parameters and position data. The proposed method allows classifying the data points according to positive or negative SFN gain with an accuracy of 0.97.

中文翻译：

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从 MFN 到 SFN：通过机器学习进行性能预测


在过去的十年中，数字地面电视（DTT）系统从多频网络（MFN）向单频网络（SFN）的转变已成为现实。 SFN 提供了与 MFN 相关的多种优势，例如更有效地管理无线电频谱、均匀化网络参数以及潜在的 SFN 增益。然而，由于需要进行多次测量活动，并且需要对最终 SFN 系统进行微调以确保所需的服务质量，因此过渡过程对于运营商来说可能很麻烦。为了避免耗时的现场测量并降低与 SFN 实施相关的成本，本文旨在通过机器学习（ML）算法根据传统 MFN 和位置数据来预测 SFN 系统的性能。提出了一种基于分类和回归的ML级联结构来预测SFN电场强度、调制误差率和增益。该模型的训练和测试过程使用比利时根特 SFN/MFN 试验的数据集进行。对多种算法进行了调整和比较，以提取数据模式并选择最准确的算法。从 MFN 参数和位置数据开始，确定系数 (R 2 ) 为 0.93，调制误差率为 0.98，SFN 增益为 0.89，获得了预测 SFN 电场强度的最佳性能。所提出的方法允许根据正或负 SFN 增益对数据点进行分类，精度为 0.97。