Estimating parameters of stochastic radio channel models based on new measurement data is an arduous task usually involving multiple steps such as multipath extraction and clustering. We propose two different machine learning methods, one based on approximate Bayesian computation (ABC) and the other on deep learning, for fitting stochastic channel models to data directly. The proposed methods make use of easy-to-compute summary statistics of measured data instead of relying on extracted multipath components. Moreover, the need for post-processing of the extracted multipath components is omitted. Taking the polarimetric propagation graph model as an example stochastic model, we present relevant summaries and evaluate the performance of the proposed methods on simulated and measured data. We find that the methods are able to learn the parameters of the model accurately in simulations. Applying the methods on 60 GHz indoor measurement data yields parameter estimates that generate averaged power delay profile from the model that fits the data.

中文翻译：

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从摘要中学习随机无线电信道模型的参数

基于新的测量数据估计随机无线电信道模型的参数是一项艰巨的任务，通常涉及多个步骤，例如多径提取和聚类。我们提出了两种不同的机器学习方法，一种基于近似贝叶斯计算（ABC），另一种基于深度学习，用于将随机通道模型直接拟合到数据。所提出的方法利用易于计算的测量数据汇总统计信息，而不是依赖于提取的多径分量。而且，省略了对提取的多径分量进行后处理的需要。以极化传播图模型为示例随机模型，我们给出了相关摘要并评估了所提出方法在模拟和测量数据上的性能。我们发现这些方法能够在仿真中准确地学习模型的参数。将这些方法应用于60 GHz室内测量数据可得出参数估计值，该估计值可从拟合数据的模型中生成平均功率延迟曲线。