Intelligent Blockage Recognition using Cellular mmWave Beamforming Data: Feasibility Study

Joint Communication and Sensing (JCAS) is envisioned for 6G cellular networks, where sensing the operation environment, especially in presence of humans, is as important as the high-speed wireless connectivity. Sensing, and subsequently recognizing blockage types, is an initial step towards signal blockage avoidance. In this context, we investigate the feasibility of using human motion recognition as a surrogate task for blockage type recognition through a set of hypothesis validation experiments using both qualitative and quantitative analysis (visual inspection and hyperparameter tuning of deep learning (DL) models, respectively). A surrogate task is useful for DL model testing and/or pre-training, thereby requiring a low amount of data to be collected from the eventual JCAS environment. Therefore, we collect and use a small dataset from a 26 GHz cellular multi-user communication device with hybrid beamforming. The data is converted into Doppler Frequency Spectrum (DFS) and used for hypothesis validations. Our research shows that (i) the presence of domain shift between data used for learning and inference requires use of DL models that can successfully handle it, (ii) DFS input data dilution to increase dataset volume should be avoided, (iii) a small volume of input data is not enough for reasonable inference performance, (iv) higher sensing resolution, causing lower sensitivity, should be handled by doing more activities/gestures per frame and lowering sampling rate, and (v) a higher reported sampling rate to STFT during pre-processing may increase performance, but should always be tested on a per learning task basis.

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication. The millimeter wave spectrum is part of 5G and covers frequencies between 30 and 300 GHz corresponding to wavelengths ranging from 10 to 1 mm. Although millimeter wave is often considered as a communication medium, it has also proved to be an excellent 'sensor', thanks to its narrow beams, operation across a wide bandwidth, and interaction with atmospheric constituents. In this paper, which is to the best of our knowledge the first review that completely covers millimeter wave sensing application pipelines, we provide a comprehensive overview and analysis of different basic application pipeline building blocks, including hardware, algorithms, analytical models, and model evaluation techniques. The review also provides a taxonomy that highlights different millimeter wave sensing application domains. By performing a thorough analysis, complying with the systematic literature review methodology and reviewing 165 papers, we not only extend previous investigations focused only on communication aspects of the millimeter wave technology and using millimeter wave technology for active imaging, but also highlight scientific and technological challenges and trends, and provide a future perspective for applications of millimeter wave as a sensing technology.