A Novel Approach for Inter-User Distance Estimation in 5G mmWave Networks Using Deep Learning

Accurate localization of devices in 5G cellular networks is of that utmost importance. This is because location information is a key component of a variety of new emerging applications. In particular, collocation (or co-location) refers to the idea of identifying devices that are located within a certain range from one another. In this paper, we propose a novel technique for inter-user distance estimation that uses low-resolution and high-resolution beam energy-based images as location fingerprints. Our approach uses the beam energy-based images generated by different users to estimate the distance between each pair of them. Nevertheless, we explore the idea of using a deep learning technique referred to as super resolution applied on low-resolution beam energy-based images to enhance their resolution, thus identify collocated users with an accuracy comparable to that of higher resolution ones. More specifically, throughout our experiments, we generate images of resolution $4\times 4$ and $8\times 8$ and use these for distance estimation between users. Afterwards, we apply super resolution on images with size $4\times 4$ to improve their resolution, and compare their results to the ones obtained with the original $8\times 8$ images. For an area roughly equal to $60\times 30\ \mathrm{m}$ , our proposed approach reaches an average mean squared error equal to 0.13 m. We also demonstrate how our proposed approach outperforms the conventional ones that rely on user location detection to measure the inter-user distance.