Positioning Based IntraFrequency Handover in Indoor Cellular Network for Ultra Reliable Communications Assisted by Radio Maps We propose two positioning based intrafrequency handover methods assisted by radio maps. The objective is to minimize the number of handovers in a usecase consisting of a robot equipped with a positioning device, moving along predetermined routes, in two indoor scenarios. The proposed methods predict the next position of the robot. The position is input to functions, constructed by symbolic regression from field measurements, that describe the radio maps for each cell, mapping positions to received powers. The function returning the highest power determines the serving cell. With one of the proposed methods, which implements a neural network, we manage to significantly reduce the number of handovers performed along the routes of the robot. The reduction in handovers allows to save resources, and scale up the use of simultaneous transmissions, which are needed to minimize handover setup times, making possible ultra reliable communications in cellular networks.

RoboMapper: An Automated Signal Mapping Robot for RSSI Fingerprinting With the everincreasing numbers of mobile devices, locationbased services became a crucial part of mobile development. Many indoor location detection systems are developed to solve positioning problem where satellitebased solutions prone to failure. Among many proposed solutions, fingerprinting technique proved to be the most reliable approach for indoor location. However, it comes with a cost; it entails a timeconsuming learning phase which should be repeated many times during the systemu0027s life time to preserve system accuracy. Thus, we propose an automated signal mapping robot called RoboMapper to alleviate timeconsuming nature of the learning phase of fingerprinting technique. With the help of its accurate distance keeping mechanisms, RoboMapper can construct the signal map of the environment so that the created map can be used for user positioning. Our findings indicate that using RoboMapper 2.68meter positioning accuracy with 70% probability can be achieved.