QUASAR: A Quantum-Classical Neural Network for SAR Satellite Physical-Layer Authentication· QUASAR：适用于X波段SAR卫星物理层认证的量子-经典神经网络
X-band SAR satellites (8-12 GHz) play a critical role in disaster response, environmental monitoring, and military intelligence. Yet, they lack robust physical-layer authentication (PLA), a security layer orthogonal to cryptographic solutions. Existing PLA systems, typically based on radio-frequency fingerprinting, are often limited to sub-6 GHz frequencies and rely on classical deep learning. However, this approach underfits the IQ phase nonlinearities that distinguish satellite hardware. In this paper, we present QUASAR, to the best of our knowledge the first quantum-classical hybrid architecture that fuses a CNN spectrogram encoder with a variational quantum circuit (VQC) to provide PLA to X-band SAR signals. Our solution enjoys two distinctive features: (i) it is markedly more data-eff
结合量子与经典神经网络，实现X波段SAR卫星物理层认证的高效解决方案。
- 核心方法
- QUASAR采用量子-经典混合架构，融合了CNN频谱图编码器与变分量子电路（VQC），以提高对X波段SAR信号的认证能力。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- X波段SAR卫星缺乏可靠的物理层认证方法，现有方法主要适用于较低频率且未能充分处理卫星硬件特征的IQ相位非线性。
- 关键实验
- 通过真实X波段SAR信号数据集测试，QUASAR在小样本量下展现出优于传统方法的认证准确率和鲁棒性。
- 主要贡献
- 提出首个适用于X波段SAR卫星物理层认证的量子-经典混合神经网络模型QUASAR，提高了认证性能的同时减少所需数据量。
- 意义与局限
- 该研究为卫星通信提供了一种新的安全认证方法，有望增强灾难响应、环境监测和军事情报等领域的信息安全性。但目前的量子计算技术限制了其实用化程度。