  Wireless Spiking neural networks (WSNNs) allow energy-efficient
device-to-device (D2D) or vehicle-to-everything (V2X) communications,
especially while considering edge intelligence and learning for beyond 5G and
6G systems. Recent research work has revealed that distributed wireless SNNs
(DWSNNs) show good performance in terms of inference accuracy and low energy
consumption of edge devices, under the constraints of limited bandwidth and
spike loss probability. In this work, we focus on neuromorphic, AI-native
transmission techniques for DWSNNs, quantitatively evaluating the features of
different coding algorithms that can be viewed as impulse radio modulations.
Specifically, the main contribution of this work is the evaluation of
information-theoretic measures that may help in quantifying performance
trade-offs among existing neuromorphic coding techniques.
