DNN-based Localization from Channel Estimates: Feature Design and Experimental Results

Autor:	Ferrand, Paul, Decurninge, Alexis, Guillaud, Maxime
Rok vydání:	2020
Předmět:	Computer Science - Networking and Internet Architecture Computer Science - Information Theory Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We consider the use of deep neural networks (DNNs) in the context of channel state information (CSI)-based localization for Massive MIMO cellular systems. We discuss the practical impairments that are likely to be present in practical CSI estimates, and introduce a principled approach to feature design for CSI-based DNN applications based on the objective of making the features invariant to the considered impairments. We demonstrate the efficiency of this approach by applying it to a dataset constituted of geo-tagged CSI measured in an outdoors campus environment, and training a DNN to estimate the position of the UE on the basis of the CSI. We provide an experimental evaluation of several aspects of that learning approach, including localization accuracy, generalization capability, and data aging. Comment: Submitted to Globecom 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2004.00363 Zobrazit plný text záznamu View this record from Arxiv