标题： 基于图像超分辨率的双基地ISAC信号增强 显示英文标题

标题： Image Super-Resolution-Based Signal Enhancement in Bistatic ISAC

摘要： 双站集成感知与通信（ISAC）有望成为下一代通信网络中的核心技术，例如超越5G（B5G）和6G，通过在不显著修改现有基础设施的情况下同时执行感知和通信功能。尽管其具有广阔的潜力，但双站协作感知的主要挑战在于感知精度的下降，这主要是由于复杂环境中高反射损耗导致的接收信号本身较弱。传统方法主要依赖自适应滤波技术，通过动态调整滤波器系数来提高信噪比（SNR）。然而，这些方法往往难以有效适应日益复杂和多样的网络拓扑。为了解决这些问题，我们提出了一种基于图像超分辨率的信号增强（ISR-SE）框架，显著提高了ISAC信号的识别和恢复能力。具体来说，我们首先通过对接收到的信号应用短时傅里叶变换（STFT），进行时频分析，生成包含频率、幅度和相位成分的频谱图。这些成分随后被映射为RGB图像，其中每个通道代表一个提取的特征，从而实现了对信号结构更直观和信息量更大的可视化。为了增强这些RGB图像，我们设计了一个改进的去噪网络，结合了UNet架构和扩散模型的优势。这种混合架构利用UNet的多尺度特征提取能力和扩散模型的生成能力，进行有效的图像去噪，从而在低SNR条件下提高信号表示的质量和清晰度。 显示英文摘要

摘要： Bistatic Integrated Sensing and Communication (ISAC) is poised to become a cornerstone technology in next-generation communication networks, such as Beyond 5G (B5G) and 6G, by enabling the concurrent execution of sensing and communication functions without requiring significant modifications to existing infrastructure. Despite its promising potential, a major challenge in bistatic cooperative sensing lies in the degradation of sensing accuracy, primarily caused by the inherently weak received signals resulting from high reflection losses in complex environments. Traditional methods have predominantly relied on adaptive filtering techniques to enhance the Signal-to-Noise Ratio (SNR) by dynamically adjusting the filter coefficients. However, these methods often struggle to adapt effectively to the increasingly complex and diverse network topologies. To address these challenges, we propose a novel Image Super-Resolution-based Signal Enhancement (ISR-SE) framework that significantly improves the recognition and recovery capabilities of ISAC signals. Specifically, we first perform a time-frequency analysis by applying the Short-Time Fourier Transform (STFT) to the received signals, generating spectrograms that capture the frequency, magnitude, and phase components. These components are then mapped into RGB images, where each channel represents one of the extracted features, enabling a more intuitive and informative visualization of the signal structure. To enhance these RGB images, we design an improved denoising network that combines the strengths of the UNet architecture and diffusion models. This hybrid architecture leverages UNet's multi-scale feature extraction and the generative capacity of diffusion models to perform effective image denoising, thereby improving the quality and clarity of signal representations under low-SNR conditions.