  Integrated sensing and communication (ISAC) in wireless systems has emerged
as a promising paradigm, offering the potential for improved performance,
efficient resource utilization, and mutually beneficial interactions between
radar sensing and wireless communications, thereby shaping the future of
wireless technologies. In this work, we present two novel methods to address
the joint angle of arrival and angle of departure estimation problem for
bistatic ISAC systems. Our proposed methods consist of a deep learning (DL)
solution leveraging complex neural networks, in addition to a parameterized
algorithm. By exploiting the estimated channel matrix and incorporating a
preprocessing step consisting of a coarse timing estimation, we are able to
notably reduce the input size and improve the computational efficiency. In our
findings, we emphasize the remarkable potential of our DL-based approach, which
demonstrates comparable performance to the parameterized method that explicitly
exploits the multiple-input multiple-output (MIMO) model, while exhibiting
significantly lower computational complexity.
