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SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensing
To address the above challenges, we first establish connections between the satellites by modeling their sensing data as a graph and devising a graph neural network-based algorithm to achieve effective spectrum sensing.
Neuromorphic In-Context Learning for Energy-Efficient MIMO Symbol Detection
In-context learning (ICL), a property demonstrated by transformer-based sequence models, refers to the automatic inference of an input-output mapping based on examples of the mapping provided as context.
Stochastic Spiking Attention: Accelerating Attention with Stochastic Computing in Spiking Networks
Spiking Neural Networks (SNNs) have been recently integrated into Transformer architectures due to their potential to reduce computational demands and to improve power efficiency.
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