  With the fast growing demand of location-based services in indoor
environments, indoor positioning based on fingerprinting has attracted a lot of
interest due to its high accuracy. In this paper, we present a novel deep
learning based indoor fingerprinting system using Channel State Information
(CSI), which is termed DeepFi. Based on three hypotheses on CSI, the DeepFi
system architecture includes an off-line training phase and an on-line
localization phase. In the off-line training phase, deep learning is utilized
to train all the weights of a deep network as fingerprints. Moreover, a greedy
learning algorithm is used to train the weights layer-by-layer to reduce
complexity. In the on-line localization phase, we use a probabilistic method
based on the radial basis function to obtain the estimated location.
Experimental results are presented to confirm that DeepFi can effectively
reduce location error compared with three existing methods in two
representative indoor environments.
