A physical-layer modulator is a vital component for an IoT gateway to map the
symbols to signals. However, due to the soldered hardware chipsets on the
gateway's motherboards or the diverse toolkits on different platforms for the
software radio, the existing solutions either have limited extensibility or are
platform-specific. Such limitation is hard to ignore when modulation schemes
and hardware platforms have become extremely diverse. This paper presents a new
paradigm of using neural networks as an abstraction layer for physical layer
modulators in IoT gateway devices, referred to as NN-defined modulators. Our
approach addresses the challenges of extensibility and portability for multiple
technologies on various hardware platforms. The proposed NN-defined modulator
uses a model-driven methodology rooted in solid mathematical foundations while
having native support for hardware acceleration and portability to
heterogeneous platforms. We conduct the evaluation of NN-defined modulators on
different platforms, including Nvidia Jetson Nano and Raspberry Pi. Evaluations
demonstrate that our NN-defined modulator effectively operates as conventional
modulators and provides significant efficiency gains (up to $4.7\times$ on
Nvidia Jetson Nano and $1.1\times$ on Raspberry Pi), indicating high
portability. Furthermore, we show the real-world applications using our
NN-defined modulators to generate ZigBee and WiFi packets, which are compliant
with commodity TI CC2650 (ZigBee) and Intel AX201 (WiFi NIC), respectively.

使用神经网络作为物理层调制器的抽象层，解决了物理层调制器在物联网网关设备中的可扩展性和可移植性的挑战，通过在不同硬件平台上的评估，展示了高度的可移植性和效率提升。