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.
翻译:神经地貌计算是一种新兴技术,它支持对需要高效率在线推断和/或控制的应用进行事件驱动的数据处理。最近的工作引入了神经地貌通信的概念,根据这一概念,神经地貌计算与脉冲无线电(IR)传输相结合,在无线互联网电话网络(IoT)中实施低能和低长远程推断。在本文中,我们引入了神经地貌综合遥感和通信(N-ISAC),这是一种新颖的解决方案,有助于高效率的在线数据解码和雷达感测。N-ISAC利用一种通用的IR波形来传递数字信息和探测雷达目标的存在或不存在的双重目的。在接收器中安装了一个闪烁式神经网络(SNN),用于对数字数据数据数据数据进行解码,并直接利用接收的信号探测雷达目标。SNNN的运行通过平衡数据通信和雷达遥感的性能指标优化,突出两个应用之间的协同作用和利弊。