  We investigate the potential of autoencoders (AEs) for building a joint
communication and sensing (JCAS) system that enables communication with one
user while detecting multiple radar targets and estimating their positions.
Foremost, we develop a suitable encoding scheme for the training of the AE and
for targeting a fixed false alarm rate of the target detection during training.
We compare this encoding with the classification approach using one-hot
encoding for radar target detection. Furthermore, we propose a new training
method that complies with possible ambiguities in the target locations. We
consider different options for training the detection of multiple targets. We
can show that our proposed approach based on permuting and sorting can enhance
the angle estimation performance so that single snapshot estimations with a low
standard deviation become possible. We outperform an ESPRIT benchmark for small
numbers of measurement samples.
