Neural-Network-Assisted UE Localization Using Radio-Channel Fingerprints in LTE Networks

In this paper, a novel fingerprint-based localization technique is proposed, which is applicable for positioning user equipments (UEs) in cellular communication networks such as the long-term-evolution (LTE) system. This technique utilizes a unique mapping between the characteristics of a radio channel formulated as a fingerprint vector and a geographical location. A feature-extraction algorithm is applied to selecting channel parameters with non-redundant information that are calculated from the LTE down-link signals. A feedforward neural network with the input of fingerprint vectors and the output of UEs' known locations is trained and used by UEs to estimate their positions. The results of experiments conducted in an in-service LTE system demonstrate that by using only one LTE eNodeB, the proposed technique yields a median error distance of 6 and 75 meters in indoor and outdoor environments, respectively. This localization technique is applicable in the cases where the Global Navigation Satellite System (GNSS) is unavailable, e.g., in indoor environments or in dense-urban scenarios with closely spaced skyscrapers heavily blocking the line-of-sight paths between a UE and GNSS satellites.