DNN-based Localization from Channel Estimates: Feature Design and Experimental Results
This work addresses localization challenges for cellular network users, but it is incremental as it builds on existing DNN methods with specific feature design.
The paper tackles the problem of improving localization accuracy in Massive MIMO cellular systems by designing deep neural network features that are invariant to practical impairments in channel state information estimates, achieving enhanced performance in an outdoor campus environment.
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.