Static Human Position Classification from Indoor mmWave Radio RSSI Measurements

With the rapid development of millimeter wave (mmWave) wireless systems, there is an increasing demand for joint communications and sensing solutions. Indoor human position estimation using wireless fidelity (WiFi) radios may be helpful to provide beyond communication applications such as e-Health, smart buildings, human tracking, etc. under sixth generation (6G) wireless systems. Moreover, WiFi sensing in-formation has the potential to improve communication itself. In the literature, the received signal strength indicator (RSSI)-based methods have been studied but with coarse results due to the usage of omnidirectional antennas. Recent WiFi sensing approaches employ vendor-specific channel state information (CSI) to obtain reliable indoor positioning. In this work, we propose a feed-forward neural network (FNN)-based indoor position estimation framework using RSSI measurements from indoor radio beamforming communication procedure. The acquired RSSI characteristic information from the exhaustive mmWave beam selection process serves as distinctive fingerprints to estimate indoor static human positions. We construct a dataset with obtained RSSI fingerprints for subject positions along LoS, nLoS, and the empty room environment. We obtain a position estimation model using FNN and the dataset. Our results show that the FNN-based framework predicts indoor static human positions using RSSI measurements at an Fl-score of 0.86 and accuracy of 0.95. Moreover, the model from such a framework is also robust to distinguish symmetric static positions with respect to the LoS link during mm Wave communication.