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

2025-09-15

AI Summary

Existing ISAC systems for scatterer sensing typically rely on waveform/hardware modifications or conventional signal processing, suffering from poor compatibility and limited localization accuracy. This paper proposes CSIYOLO, the first framework to formulate single-base-station-to-user-equipment (BS-UE) channel state information (CSI)-based scatterer localization as an image-based object detection problem using the YOLO architecture. CSIYOLO comprises three key components: a CSI feature adaptation module, an anchor-guided multi-scale detection network, and a noise-injection training strategy explicitly designed to mitigate channel estimation errors. Crucially, it enables plug-and-play scatterer sensing without altering waveforms or hardware. Experiments demonstrate that CSIYOLO significantly outperforms existing methods in multi-scatterer scenarios and under varying channel estimation errors, achieving high localization accuracy, low computational complexity, and strong system compatibility and practicality.

Technology Category

• Search and Optimization: Local Search
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

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.

Problem

• Poor compatibility of scatter sensing with current communication systems
• Limited sensing accuracy in integrated sensing and communication systems
• Dependence on waveform/hardware modifications or traditional signal processing

Innovation

• Anchor-based scatter detection using YOLO-inspired image processing
• CSI-based localization algorithm with extracted scatter parameters
• Noise injection training for robustness against channel errors