论文标题
神经形态综合感应和通信
Neuromorphic Integrated Sensing and Communications
论文作者
论文摘要
神经形态计算是一项新兴技术，该技术为需要有效的在线推理和/或控制的应用程序提供了以事件为导向的数据处理。最近的工作介绍了神经形态通信的概念，在该概念中，神经形态计算与Impulse Radio（IR）传输集成在一起，以实现无线网络（IOT）网络中低能量和低延迟远程推断。在本文中，我们介绍了神经形态综合传感和通信（N-ISAC），这是一种新的解决方案，可实现有效的在线数据解码和雷达传感。 N-ISAC利用了一个常见的IR波形，以传达数字信息并检测存在或不存在雷达靶标的双重目的。在接收器上部署了尖峰神经网络（SNN），以解码数字数据并使用接收的信号检测雷达目标。通过平衡数据通信和雷达传感的性能指标，突出了两个应用程序之间的协同作用和权衡，可以优化SNN操作。
Neuromorphic computing is an emerging technology that support event-driven data processing for applications requiring efficient online inference and/or control. Recent work has introduced the concept of neuromorphic communications, whereby neuromorphic computing is integrated with impulse radio (IR) transmission to implement low-energy and low-latency remote inference in wireless Internet-of-Things (IoT) networks. In this paper, we introduce neuromorphic integrated sensing and communications (N-ISAC), a novel solution that enables efficient online data decoding and radar sensing. N-ISAC leverages a common IR waveform for the dual purpose of conveying digital information and of detecting the presence or absence of a radar target. A spiking neural network (SNN) is deployed at the receiver to decode digital data and to detect the radar target using directly the received signal. The SNN operation is optimized by balancing performance metrics for data communications and radar sensing, highlighting synergies and trade-offs between the two applications.
