SpikeWFM: Spiking-Aided Wireless Foundation Model for Robust Channel Prediction
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-training on large-scale datasets spanning various wireless environments to learn a unified embedding that supports a wide range of downstre...
| Authors | Liwen Jing, Yisha Lu, Tingting Yang et al. |
|---|---|
| Primary category | eess.SP |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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