CSIYOLO: An Intelligent CSI-based Scatter Sensing Framework for Integrated Sensing and Communication Systems
This addresses the problem of poor compatibility and limited sensing accuracy in scatter sensing for applications like autonomous driving, though it is incremental as it builds on YOLO architectures for a specific domain.
The paper tackles scatter localization in integrated sensing and communication systems by proposing CSIYOLO, a framework that uses estimated channel state information from a single base station-user equipment pair without modifying waveforms or hardware, achieving significantly higher localization accuracy than existing methods with low complexity.
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.