AI RESEARCH
SpikeWFM: Spiking-Aided Wireless Foundation Model for Robust Channel Prediction
arXiv CS.AI
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ArXi:2606.00120v1 Announce Type: cross This paper proposes SpikeWFM, a novel hybrid architecture that integrates spiking neural networks (SNNs) with conventional artificial neural network (ANN)-based transformers for wireless foundation models (WFMs). Inspired by the noise-robust and energy-efficient information processing in the human brain, SpikeWFM aims to enhance the resilience of WFMs against noise and interference while maintaining strong generalization capabilities across diverse wireless scenarios. Drawing from the success of large language models, WFMs leverage self-supervised pre.