Spectral-Based Proactive Blockage Detection for Sub-THz Communications

Human body blockage is one of the main reasons prohibiting the adoption of modern millimeter wave (mmWave, 30-70 GHz) and future sub-terahertz (sub-THz, 100-300 GHz) cellular systems. To utilize system-level blockage avoidance solutions, such as multiconnectivity, one must be able to detect blockage events prior to their occurrence. To this end, recent approaches have utilized machine learning algorithms operating in the time domain. In our paper, we advocate for the use of spectral representation, where the gap between pre-blockage and non-blockage periods is much wider and directly measurable. By utilizing this idea, we developed a simple proactive blockage detection algorithm for indoor deployment of sub-THz systems and evaluated it using blockage detection probability, mean time to blockage, and false alarm rate. Our results obtained using measured traces of blockage events at 156 GHz show that it is capable of detecting a blockage event at least 50 ms prior to its occurrence. Compared to other approaches, it is characterized by a false alarm rate of less than one events/s making it robust even in fading environments.