ISAC-NET: Model-driven Deep Learning for Integrated Passive Sensing and
  Communication
Wangjun Jiang Dingyou Ma Zhiqing Wei Zhiyong Feng Ping Zhang
Recent advances in wireless communication with the enormous demands of
sensing ability have given rise to the integrated sensing and communication
(ISAC) technology, among which passive sensing plays an important role. The
main challenge of passive sensing is how to achieve high sensing performance in
the condition of communication demodulation errors. In this paper, we propose
an ISAC network (ISAC-NET) that combines passive sensing with communication
signal detection by using model-driven deep learning (DL). Dissimilar to
existing passive sensing algorithms that first demodulate the transmitted
symbols and then obtain passive sensing results from the demodulated symbols,
ISAC-NET obtains passive sensing results and communication demodulated symbols
simultaneously. Different from the data-driven DL method, we adopt the
block-by-block signal processing method that divides the ISAC-NET into the
passive sensing module, signal detection module and channel reconstruction
module. From the simulation results, ISAC-NET obtains better communication
performance than the traditional signal demodulation algorithm, which is close
to OAMP-Net2. Compared to the 2D-DFT algorithm, ISAC-NET demonstrates
significantly enhanced sensing performance. In summary, ISAC-NET is a promising
tool for passive sensing and communication in wireless communications.