Data-driven Simulation of Wireless Communication Signal Strength in Indoor Environments.

Prediction of the Received Signal Strength Indicator (RSSI) distribution is a very important task. However, most of the current research is on methods that complement the RSSI distribution for beacons that actually collect data. The most common method for fully online simulation without beacons is based on physical Ray-Tracing. However, the Ray-Tracing model requires the determination of the attenuation rate of the wall. This makes it difficult for ordinary people to perform the simulation. Also, without measuring the actual RSSI, it is impossible to know whether the simulation results are appropriate for the actual environment or not. To address these issues, we propose a data-driven RSSI simulation method. RSSI can be collected by various devices, and it is easy for the general public to obtain RSSI for each location. The simulation is based on the actual RSSI data, so that the simulation can be performed in a realistic environment. In this paper, we have realized a data-driven simulation that matches the environment by learning the attenuation of RSSI derived from the environment and actual data using Generative Adversarial Network(GAN). In order to conduct experiments in a real environment, the simulation model is trained in an office environment, and its accuracy is evaluated using actual RSSI values. As a result, the average absolute error of RSSI values was improved by 8% and the average positioning error of indoor localization was improved by 19% compared with the simulation using the radio propagation formula.