  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.
