PolymoRF：通过物理层深度学习的多态无线接收器
PolymoRF: Polymorphic Wireless Receivers Through Physical-Layer Deep Learning
摘要：
当今的无线技术在很大程度上基于不灵活的设计，这使得它们效率低下，容易受到各种无线攻击。为了解决这个关键问题，无线接收器将需要（i）实时推断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调传入波形。在本文中，我们介绍了PolymoRF，这是一种基于深度学习的多态接收器，能够根据推断的波形参数实时的重新配置自己。我们的关键技术创新是（i）一种新型的嵌入式深度学习架构，称为RFNet，可以解决关键的波形推断问题;（ii）一种通用的硬件/软件架构，将RFNet与无线电组件和信号处理集成在一起。我们在一个定制软件定义的无线电平台上对PolymoRF进行了原型设计，并通过大量的空中实验表明：（i）RFNet实现了与最先进的精度相似的精度，但延迟和硬件减少了52倍和8倍;（ii）PolymoRF实现了87%的吞吐量，这是第一次证明多态接收器是可行和有效的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
