Learning Human-Blockage Direction Prediction from Indoor mmWave Radio Measurements

Millimeter wave (mmWave) beamforming is a vital component of the fifth generation (5G) new radio (NR) and beyond wireless communication systems. The usage of mmWave narrow beams encounters frequent signal attenuation due to random human blockages in indoor environments. Human blockage predictions can jointly improve the signal quality as well as passively sense human activities during mmWave communication. Human sensing using wireless fidelity (WiFi) systems has earlier been studied using receiver signal strength indicator (RSSI) signal level fluctuations based on distance measurements. Other conventional approaches using cameras, lidars, radars, etc. require additional hardware deployments. Current device-free WiFi sensing approaches use vendor-specific channel state information to obtain fine-grained human blockage predictions. Our novelty in this work is to obtain fine-grained human blockage direction predictions in mmWave spectrum, using a time series of RSSI measurements and build fingerprints. We perform experiments to construct a Human Millimetre-wave Radio Blockage Detection (HuMRaBD) dataset and observe human influence in different radio beam directions during each radio initial access procedure. We design a multi layer perceptron (MLP) framework to analyze the HuMRaBD dataset over coarse-grained and fine-grained mmWave blockage directions from static and dynamic human movements. The results show that our trained MLP-trained models can simultaneously sense multiple indoor human radio-blockage directions at an average F1 score of 0.84 and area under curve (AUC) score of 0.95 during mmWave communication.