让下一篇重要论文，不再错过。

原文摘要

Wake-up radios (WURs) reduce energy consumption by continuously monitoring the channel for a wake-up signal (WUS) while the main receiver remains asleep. Neuromorphic computing offers a promising approach to ultra-low-power WUS detection through sparse, event-driven processing. However, realizing these benefits requires spike-like inputs, which conventional orthogonal frequency-division multiplexing (OFDM) waveforms do not naturally provide. To enable neuromorphic WUS detection in OFDM-based systems, we introduce a neuromorphic wake-up signal (nWUS) that can be generated by a standard OFDM transmitter. The proposed design superposes frequency-domain vectors with linear phase progressions across the OFDM subcarriers, producing a time-domain waveform with sparse amplitude peaks at predefined positions. These peaks encode the wake-up signature in a spike-like representation that facilitates neuromorphic processing at the receiver. Building on this structure, we then propose a noncoherent neuromorphic detector that encodes received peaks into spikes and performs sparse correlation only upon spike events. Simulations over a 3GPP urban macro channel show that the proposed approach outperforms on-off keying (OOK) baselines in detection probability while achieving up to 43.5x lower computational cost. During idle periods, event-driven processing further reduces correlation operations by 86% relative to nWUS reception.