Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networks

TL;DR

This paper presents a generative AI-based data augmentation method using diffusion models to improve sensing and communication in ISAC networks, especially when training data is limited or unevenly distributed.

Contribution

It introduces a novel diffusion model-based data augmentation scheme for ISAC systems, enhancing data quality and quantity to improve sensing accuracy and target detection.

Findings

Detection performance improved by up to 70%

Enhanced data quality leads to more reliable sensing

Robustness demonstrated in limited data scenarios

Abstract

Integrated sensing and communication (ISAC) uses the same software and hardware resources to achieve both communication and sensing functionalities. Thus, it stands as one of the core technologies of 6G and has garnered significant attention in recent years. In ISAC systems, a variety of machine learning models are trained to analyze and identify signal patterns, thereby ensuring reliable sensing and communications. However, considering factors such as communication rates, costs, and privacy, collecting sufficient training data from various ISAC scenarios for these models is impractical. Hence, this paper introduces a generative AI (GenAI) enabled robust data augmentation scheme. The scheme first employs a conditioned diffusion model trained on a limited amount of collected CSI data to generate new samples, thereby expanding the sample quantity. Building on this, the scheme further utilizes another diffusion model to enhance the sample quality, thereby facilitating the data augmentation in scenarios where the original sensing data is insufficient and unevenly distributed. Moreover, we propose a novel algorithm to estimate the acceleration and jerk of signal propagation path length changes from CSI. We then use the proposed scheme to enhance the estimated parameters and detect the number of targets based on the enhanced data. The evaluation reveals that our scheme improves the detection performance by up to 70%, demonstrating reliability and robustness, which supports the deployment and practical use of the ISAC network.