论文标题
PolyMorf：通过物理层深度学习多态无线接收器
PolymoRF: Polymorphic Wireless Receivers Through Physical-Layer Deep Learning
论文作者
论文摘要
当今的无线技术主要基于僵化的设计，这使它们效率低下且容易受到各种无线攻击。为了解决此关键问题，无线接收器需要（i）发射器当前使用的物理层参数；如果需要，（ii）更改其硬件和软件结构以解码传入的波形。在本文中，我们介绍了PolyMorf，这是一种基于深度学习的多态性接收器，能够根据推断的波形参数实时重新配置自身。我们的关键技术创新是（i）一种新颖的深度学习架构，称为RFNET，它可以解决关键波形推理问题的解决方案； （ii）将RFNET与无线电组件和信号处理集成的广义硬件/软件体系结构。我们在自定义软件定义的无线电平台上原型Polymorf进行了原型polymorf，并通过广泛的空中实验显示（i）RFNET的精度与最先进的精度相似，但具有52X和8X延迟和硬件的降低； （ii）Polymorf在完美知识的甲骨文系统的87％之内实现了吞吐量，因此首次证明了多态接收器是可行且有效的。
Today's wireless technologies are largely based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. To address this key issue, wireless receivers will need to (i) infer on-the-fly the physical-layer parameters currently used by transmitters; and if needed, (ii) change their hardware and software structures to demodulate the incoming waveform. In this paper, we introduce PolymoRF, a deep learning-based polymorphic receiver able to reconfigure itself in real time based on the inferred waveform parameters. Our key technical innovations are (i) a novel embedded deep learning architecture, called RFNet, which enables the solution of key waveform inference problems; (ii) a generalized hardware/software architecture that integrates RFNet with radio components and signal processing. We prototype PolymoRF on a custom software-defined radio platform, and show through extensive over-the-air experiments that (i) RFNet achieves similar accuracy to that of state-of-the-art yet with 52x and 8x latency and hardware reduction; (ii) PolymoRF achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible and effective.
