# CSIYOLO: An Intelligent CSI-based Scatter Sensing Framework for Integrated Sensing and Communication Systems

arXiv: https://arxiv.org/abs/2509.19335 · HTML: https://mlsanity.com/paper/2509.19335/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 68 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 30 of 31)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Multi-resolution CSI Feedback with deep learning in Massive MIMO System](https://mlsanity.com/paper/1910.14322/) (2019)
- [Robust and Precise Vehicle Localization based on Multi-sensor Fusion in Diverse City Scenes](https://mlsanity.com/paper/1711.05805/) (2017)
- [Integrating Sensing and Communications for Ubiquitous IoT: Applications, Trends and Challenges](https://mlsanity.com/paper/2104.11457/) (2021)
- [Integrated Sensing and Communication Signals Toward 5G-A and 6G: A Survey](https://mlsanity.com/paper/2301.03857/) (2023)
- [Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments](https://mlsanity.com/paper/2203.01907/) (2022)
- [Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation](https://mlsanity.com/paper/2102.11593/) (2021)
- [Interference Management for Integrated Sensing and Communication Systems: A Survey](https://mlsanity.com/paper/2403.16189/) (2024)
- [Device-Free Sensing in OFDM Cellular Network](https://mlsanity.com/paper/2108.09177/) (2021)
- [Deep Learning Based FDD Non-Stationary Massive MIMO Downlink Channel Reconstruction](https://mlsanity.com/paper/2002.09858/) (2020)
- [Neural RF SLAM for unsupervised positioning and mapping with channel state information](https://mlsanity.com/paper/2203.08264/) (2022)
- [Deep Learning Based Multi-Node ISAC 4D Environmental Reconstruction with Uplink- Downlink Cooperation](https://mlsanity.com/paper/2404.14862/) (2024)
- [Multi-Sector and Multi-Panel Performance in 5G mmWave Cellular Networks](https://mlsanity.com/paper/1808.04905/) (2018)
- [Location- and Orientation-aware Millimeter Wave Beam Selection for Multi-Panel Antenna Devices](https://mlsanity.com/paper/2203.11714/) (2022)
- [Trilateration-Based Device-Free Sensing: Two Base Stations and One Passive IRS Are Sufficient](https://mlsanity.com/paper/2205.12667/) (2022)
- [Multiple Signal Classification Based Joint Communication and Sensing System](https://mlsanity.com/paper/2211.04064/) (2022)
- [Millimeter Wave Wireless Communication Assisted Three-Dimensional Simultaneous Localization and Mapping](https://mlsanity.com/paper/2303.02617/) (2023)
- [Iterative Signal Processing for Integrated Sensing and Communication Systems](https://mlsanity.com/paper/2306.05235/) (2023)
- [Electromagnetic Property Sensing: A New Paradigm of Integrated Sensing and Communication](https://mlsanity.com/paper/2312.16428/) (2023)
- [Environment Sensing-aided Beam Prediction with Transfer Learning for Smart Factory](https://mlsanity.com/paper/2405.15339/) (2024)
- [Bayesian inference for PCA and MUSIC algorithms with unknown number of sources](https://mlsanity.com/paper/1809.10168/) (2018)
- [Joint Scattering Environment Sensing and Channel Estimation Based on Non-stationary Markov Random Field](https://mlsanity.com/paper/2302.02587/) (2023)
- [Joint Data Association, NLOS Mitigation, and Clutter Suppression for Networked Device-Free Sensing in 6G Cellular Network](https://mlsanity.com/paper/2302.08080/) (2023)
- [Doppler-Resilient Design of CAZAC Sequences for mmWave/THz Sensing Applications](https://mlsanity.com/paper/2305.07234/) (2023)
- [Cross-Domain Dual-Functional OFDM Waveform Design for Accurate Sensing/Positioning](https://mlsanity.com/paper/2311.04483/) (2023)
- [Reflection Map Construction: Enhancing and Speeding Up Indoor Localization](https://mlsanity.com/paper/2406.00563/) (2024)

## Abstract (arXiv)

ISAC is regarded as a promising technology for next-generation communication systems, enabling simultaneous data transmission and target sensing. Among various tasks in ISAC, scatter sensing plays a crucial role in exploiting the full potential of ISAC and supporting applications such as autonomous driving and low-altitude economy. However, most existing methods rely on either waveform and hardware modifications or traditional signal processing schemes, leading to poor compatibility with current communication systems and limited sensing accuracy. To address these challenges, we propose CSIYOLO, a framework that performs scatter localization only using estimated CSI from a single base station-user equipment pair. This framework comprises two main components: anchor-based scatter parameter detection and CSI-based scatter localization. First, by formulating scatter parameter extraction as an image detection problem, we propose an anchor-based scatter parameter detection method inspired by You Only Look Once architectures. After that, a CSI-based localization algorithm is derived to determine scatter locations with extracted parameters. Moreover, to improve localization accuracy and implementation efficiency, we design an extendable network structure with task-oriented optimizations, enabling multi-scale anchor detection and better adaptation to CSI characteristics. A noise injection training strategy is further designed to enhance robustness against channel estimation errors. Since the proposed framework operates solely on estimated CSI without modifying waveforms or signal processing pipelines, it can be seamlessly integrated into existing communication systems as a plugin. Experiments show that our proposed method can significantly outperform existing methods in scatter localization accuracy with relatively low complexities under varying numbers of scatters and estimation errors.