ISAC-NET: Model-driven Deep Learning for Integrated Passive Sensing and Communication

TL;DR

ISAC-NET is a model-driven deep learning framework that simultaneously enhances passive sensing and communication performance in wireless systems, outperforming traditional algorithms in sensing accuracy and demodulation robustness.

Contribution

The paper introduces ISAC-NET, a novel deep learning architecture that integrates passive sensing with communication signal detection in a unified framework.

Findings

ISAC-NET achieves better communication performance than traditional demodulation methods.

It significantly improves sensing accuracy compared to 2D-DFT algorithms.

The method performs close to OAMP-Net2 in communication tasks.

Abstract

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.