  We evaluate the influence of multi-snapshot sensing and varying
signal-to-noise ratio (SNR) on the overall performance of neural network
(NN)-based joint communication and sensing (JCAS) systems. To enhance the
training behavior, we decouple the loss functions from the respective SNR
values and the number of sensing snapshots, using bounds of the sensing
performance. Pre-processing is done through conventional sensing signal
processing steps on the inputs to the sensing NN. The proposed method
outperforms classical algorithms, such as a Neyman-Pearson-based power detector
for object detection and ESPRIT for angle of arrival (AoA) estimation for
quadrature amplitude modulation (QAM) at low SNRs.
