Access-Point Centered Window-Based Radio-Map Generation Network

Abstract

Fingerprinting is the term used to describe a common indoor radio-mapping positioning technology that tracks moving objects in real time. To use this, a substantial number of measurement processes and workflows are needed to generate a radio-map. Accordingly, to minimize costs and increase the usability of such radio-maps, this study proposes an access-point (AP)-centered window (APCW) radio-map generation network (RGN). The proposed technique extracts parts of a radio-map in the form of a window based on AP floor plan coordinates to shorten the training time while enhancing radio-map prediction accuracy. To provide robustness against changes in the location of the APs and to enhance the utilization of similar structures, the proposed RGN, which employs an adversarial learning method and uses the APCW as input, learns the indoor space in partitions and combines the radio-maps of each AP to generate a complete map. By comparing four learning models that use different data structures as input based on an actual building, the proposed radio-map learning model (i.e., APCW-based RGN) obtains the highest accuracy among all models tested, yielding a root-mean-square error value of 4.01 dBm.

Fine Tuning an AI-Based Indoor Radio Propagation Model with Crowd-Sourced Data

“…AI-based RPMs provide reduced computing time and simplicity of usage : for indoor only a simple floor plan image is required without any need of tedious vectorization. Several models have been already proposed in the literature, some for indoor and others for outdoor [1][2][3] [4]. However, almost all these AI-based models have been trained on simulated radio data.…”