Channel decoding, channel detection, channel assessment, and resource management for wireless multiple-input multiple-output (MIMO) systems are all examples of problems where machine learning (ML) can be successfully applied. In this paper, we study several ML approaches to solve the problem of estimating the spectral efficiency (SE) value for a certain precoding scheme, preferably in the shortest possible time. The best results in terms of mean average percentage error (MAPE) are obtained with gradient boosting over sorted features, while linear models demonstrate worse prediction quality. Neural networks perform similarly to gradient boosting, but they are more resource- and time-consuming because of hyperparameter tuning and frequent retraining. We investigate the practical applicability of the proposed algorithms in a wide range of scenarios generated by the Quadriga simulator. In almost all scenarios, the MAPE achieved using gradient boosting and neural networks is less than 10\%.
翻译:在本文中,我们研究了若干ML方法来解决某些预编码办法的光谱效率估计问题,最好是在尽可能短的时间内。在平均百分比差方面,取得最佳结果的是分类特征的梯度上升,而线性模型则显示的预测质量更差。神经网络与梯度加速作用相似,但由于超分计调和频繁再培训,它们的资源和时间消耗更耗。我们研究了在夸德里加模拟器产生的一系列假设情景中拟议算法的实际适用性。几乎所有情况下,使用梯度加速和神经网络实现的MAPE都不到10 ⁇ 。