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Generalization aspect of accurate machine learning models for CSI-based localization
Annals of Telecommunications ( IF 1.9 ) Pub Date : 2021-06-14 , DOI: 10.1007/s12243-021-00853-z
Abdallah Sobehy 1 , Éric Renault 2 , Paul Mühlethaler 3
Affiliation  

Localization is the process of determining the position of an entity in a given coordinate system. Due to its wide range of applications (e.g. autonomous driving, Internet-of-Things), it has gained much focus from the industry and academia. Channel State Information (CSI) has overtaken Received Signal Strength Indicator (RSSI) to achieve localization given its temporal stability and rich information. In this paper, we extend our previous work by combining classical and deep learning methods in an attempt to improve the localization accuracy using CSI. We then test the generalization aspect of both approaches in different environments by splitting the training and test sets such that their intersection is reduced when compared with uniform random splitting. The deep learning approach is a Multi Layer Perceptron Neural Network (MLP NN) and the classical machine learning method is based on K-nearest neighbors (KNN). The estimation results of both approaches outperform state-of-the-art performance on the same dataset. We illustrate that while the accuracy of both approaches deteriorates when tested for generalization, deep learning exhibits a higher potential to perform better beyond the training set. This conclusion supports recent state-of-the-art attempts to understand the behaviour of deep learning models.



中文翻译:

用于基于 CSI 的定位的准确机器学习模型的泛化方面

定位是确定实体在给定坐标系中的位置的过程。由于其广泛的应用(例如自动驾驶、物联网),它受到了业界和学术界的广泛关注。鉴于信道状态信息 (CSI) 的时间稳定性和丰富的信息,信道状态信息 (CSI) 已取代接收信号强度指示 (RSSI) 来实现定位。在本文中,我们通过结合经典和深度学习方法来扩展我们之前的工作,以尝试使用 CSI 来提高定位精度。然后,我们通过拆分训练集和测试集来测试这两种方法在不同环境中的泛化方面,以便与均匀随机拆分相比减少它们的交集。深度学习方法是多层感知器神经网络(MLP NN),经典机器学习方法是基于 K 近邻(KNN)。两种方法的估计结果都优于相同数据集上的最新性能。我们说明,虽然两种方法的准确性在进行泛化测试时都会下降,但深度学习在训练集之外表现出更高的表现潜力。这一结论支持最近了解深度学习模型行为的最新尝试。深度学习表现出更高的潜力,可以在训练集之外表现得更好。这一结论支持最近了解深度学习模型行为的最新尝试。深度学习表现出更高的潜力,可以在训练集之外表现得更好。这一结论支持最近了解深度学习模型行为的最新尝试。

更新日期:2021-06-14
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