DeepCReg: Improving Cellular-based Outdoor Localization using CNN-based Regressors

In this paper, we propose DeepCReg, a convolutional neural network based regressor, that leverages the ubiquitous cellular data to estimate the location of the user in an outdoor environment. We formulate the problem of outdoor localization of a user as a regression problem. This formulation overcomes the limitations of other neural network based classification methods which estimates the position using a grid cell of pre-specified dimensions. We regress on the position directly which leads to better scalability when the testbed area is increased. Moreover, we introduce the usage of convolutional neural networks instead of fully connected neural networks to add more robustness to small changes in the environment. We evaluate our system on two different datasets to emphasize on the scalability of our regression approach. The testbeds are of size $0.147 km^{2}$ and $1.469 km^{2}$. Our system achieves median localization error of $2.06 m$ and $2.82 m$ on each dataset respectively, outperforming current state-of-the-art outdoor cellular based systems by at least 877% improvement in the median localization error.