AI 中文总结
本文提出一种智能反射面（RIS）辅助的符合IEEE 802.15.4标准的神经形态集成感知与通信（N-ISAC）系统，采用脉冲神经网络（SNN）接收机处理HRP UWB波形，量化了RIS频率选择性的影响并分析了性能与能耗的权衡。
AI 中文摘要
神经形态计算支持事件驱动的低功耗推理，因此是资源受限无线设备上联合执行通信、感知与处理的极具吸引力的技术。脉冲无线电超宽带（IR-UWB）信号已在IEEE 802.15.4系列中标准化，广泛应用于测距和短距离通信，其与神经形态处理天然匹配，因为两者均通过稀疏时间事件（即脉冲）运行。本文研究一种符合标准的神经形态集成感知与通信（N-ISAC）系统，其中单个脉冲神经网络（SNN）接收机直接从通用IEEE 802.15.4z高脉冲重复频率（HRP）UWB波形中联合解调数字数据并检测无源雷达目标。与之前假设基本脉冲位置调制接口和简化多径信道的N-ISAC研究不同，本文考虑完整的标准化物理层以及由可重构智能表面（RIS）辅助的真实射线追踪信道。该模型考虑了基于超材料的RIS的频率选择性响应，其会色散宽带IR-UWB脉冲，可能降低通信和感知性能。数值实验量化了RIS频率选择性对UWB ISAC的影响，并表征了ISAC性能与接收机计算能耗之间的权衡关系。
英文摘要
Neuromorphic computing enables event-driven, low-power inference and is therefore an attractive technology for jointly carrying out communication, sensing, and processing at resource-constrained wireless devices. Impulse radio ultra-wideband (IR-UWB) signaling, which is standardized in the IEEE 802.15.4 family and widely deployed for ranging and short-range communication, is naturally matched to neuromorphic processing, since both operate through sparse temporal events, or spikes. In this context, this paper studies a standard-compliant neuromorphic integrated sensing and communications (N-ISAC) system in which a single spiking neural network (SNN) receiver jointly demodulates digital data and detects a passive radar target directly from a common IEEE 802.15.4z high-rate-pulse-repetition-frequency (HRP) UWB waveform. Unlike prior N-ISAC work, which assumed a basic pulse-position-modulation interface and a simplified multipath channel, we consider the full standardized physical layer together with a realistic ray-traced channel aided by a reconfigurable intelligent surface (RIS). The model accounts for the frequency-selective response of a metamaterial-based RIS, which disperses the wideband IR-UWB pulses and can degrade both communication and sensing. Numerical experiments quantify the impact of RIS frequency selectivity on UWB ISAC and characterize the trade-off between ISAC performance and receiver computation energy.
