Indoor location estimation by using MLE based algorithm on smallcell networks

This paper presents a new framework for indoor localization using third generation universal mobile telecommunication system (3G UMTS) Femtocell. The fingerprinting technique is applied to collect the RSSI values through an Android User Equipment (UE) and data is processed in real time using Message Queuing telemetry protocol (MQTT) server. To achieve better RF planning and optimization for the placement of Femto Access Point (FAP), statistical analysis is performed by normalizing and calculating the mean square error (MSE) of the acquired data. To maximize the success rate in finding the location of the person, maximum likelihood estimation (MLE) is used for tracking. Simulation was carried out both for randomly generated samples and real world test.