Learning of the cell-load in radio access networks (RANs) has to be performed within a short time period. Therefore, we propose a learning framework that is robust against uncertainties resulting from the need for learning based on a relatively small training set. To this end, we incorporate prior knowledge about the cell-load in the learning framework. For example, an inherent property of the cell-load is that it is monotonic in downlink (data) rates. To obtain additional prior knowledge we first study the feasible rate region, i.e., the set of all vectors of user rates that can be supported by the network. We prove that the feasible rate region is compact. Moreover, we show the existence of a Lipschitz function that maps feasible rate vectors to cell-load vectors. With these results in hand, we present a learning technique that guarantees a minimum approximation error in the worst-case scenario by using prior knowledge and a small training sample set. Simulations in the network simulator NS3 demonstrate that the proposed method exhibits better robustness and accuracy than standard learning techniques, especially for small training sample sets.

中文翻译：

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使用小样本集进行稳健的细胞负载学习

必须在短时间内执行无线电接入网络 (RAN) 中小区负载的学习。因此，我们提出了一个学习框架，该框架对于基于相对较小的训练集进行学习的需要而产生的不确定性具有鲁棒性。为此，我们在学习框架中加入了关于细胞负载的先验知识。例如，小区负载的一个固有特性是它在下行链路（数据）速率上是单调的。为了获得额外的先验知识，我们首先研究可行速率区域，即网络可以支持的所有用户速率向量的集合。我们证明可行率区域是紧凑的。此外，我们展示了将可行速率向量映射到单元负载向量的 Lipschitz 函数的存在。有了这些结果，我们提出了一种学习技术，通过使用先验知识和小的训练样本集，在最坏的情况下保证最小的近似误差。网络模拟器 NS3 中的模拟表明，所提出的方法表现出比标准学习技术更好的鲁棒性和准确性，尤其是对于小训练样本集。