  Radio Frequency (RF) sensing holds the potential for enabling pervasive
monitoring applications. However, modern sensing algorithms imply complex
operations, which clash with the energy-constrained nature of edge sensing
devices. This calls for the development of new processing and learning
techniques that can strike a suitable balance between performance and energy
efficiency. Spiking Neural Networks (SNNs) have recently emerged as an
energy-efficient alternative to conventional neural networks for edge computing
applications. They process information in the form of sparse binary spike
trains, thus potentially reducing energy consumption by several orders of
magnitude. Their fruitful use for RF signal processing critically depends on
the representation of RF signals in the form of spike signals. We underline
that existing spike encoding algorithms to do so generally produce inaccurate
signal representations and dense (i.e., inefficient) spike trains. In this
work, we propose a lightweight neural architecture that learns a tailored spike
encoding representations of RF channel responses by jointly reconstructing the
input and its spectral content. By leveraging a tunable regularization term,
our approach enables fine-grained control over the performance-energy trade-off
of the system. Our numerical results show that the proposed method outperforms
existing encoding algorithms in terms of reconstruction error and sparsity of
the obtained spike encodings.
