ISAC-NET: Model-driven Deep Learning for Integrated Passive Sensing and Communication
This work addresses a specific problem in wireless communications for passive sensing and communication integration, representing an incremental improvement by combining existing methods in a novel way.
The paper tackles the challenge of achieving high sensing performance despite communication demodulation errors in integrated sensing and communication (ISAC) by proposing ISAC-NET, a model-driven deep learning approach that simultaneously obtains passive sensing results and demodulated symbols, resulting in better communication performance close to OAMP-Net2 and significantly enhanced sensing performance compared to the 2D-DFT algorithm.
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.