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

arXiv: https://arxiv.org/abs/2307.15074 · HTML: https://mlsanity.com/paper/2307.15074/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 14 Jul 2023.

## Builds on (most cited first, 14 of 14)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [From Denoising to Compressed Sensing](https://mlsanity.com/paper/1406.4175/) (2014)
- [Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions](https://mlsanity.com/paper/1904.09673/) (2019)
- [An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing](https://mlsanity.com/paper/2102.12780/) (2021)
- [ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers](https://mlsanity.com/paper/1810.09082/) (2018)
- [Enabling Joint Communication and Radar Sensing in Mobile Networks -- A Survey](https://mlsanity.com/paper/2006.07559/) (2020)
- [Trainable Projected Gradient Detector for Massive Overloaded MIMO Channels: Data-driven Tuning Approach](https://mlsanity.com/paper/1812.10044/) (2018)
- [A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authentication](https://mlsanity.com/paper/2108.04436/) (2021)
- [FRaC: FMCW-Based Joint Radar-Communications System via Index Modulation](https://mlsanity.com/paper/2106.14671/) (2021)
- [Improve Radar Sensing Performance of Multiple Roadside Units Cooperation via Space Registration](https://mlsanity.com/paper/2206.10930/) (2022)
- [Framework for a Perceptive Mobile Network using Joint Communication and Radar Sensing](https://mlsanity.com/paper/1901.05558/) (2019)
- [Uplink Sensing in Perceptive Mobile Networks with Asynchronous Transceivers](https://mlsanity.com/paper/2011.09098/) (2020)
- [Iterative Signal Processing for Integrated Sensing and Communication Systems](https://mlsanity.com/paper/2306.05235/) (2023)
- [Improve Sensing and Communication Performance of UAV via Integrated Sensing and Communication](https://mlsanity.com/paper/2206.10945/) (2022)

## Built on by (most cited first, 3 of 3)

- [Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities](https://mlsanity.com/paper/2509.06968/) (2025)
- [Resolving Multi-Target Association in OFDM-based ISAC via Vision-aided Multi-Modal Learning](https://mlsanity.com/paper/2606.22195/) (2026)
- [Task-Oriented Candidate-Latent Feedback for Coarse-to-Fine Sensing in Distributed OFDM-ISAC Networks](https://mlsanity.com/paper/2608.03319/) (2026)

## Abstract (arXiv)

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.