Enhanced Demodulator for 5G NTN using Spatio-Temporal Attention Convolutional Autoencoder and Akida Brainchip SNN

This paper presents a novel approach to overcoming
challenges in 5G and 6G mobile satellite systems (MSS) in Low
Earth Orbit (LEO), focusing on Non-Line-of-Sight (NLOS) issues
in 5G Non-Terrestrial Networks (NTN) that connect directly with
handheld devices. We utilize a Convolutional Neural Network
(CNN) with a Spatio-Temporal Attention Network (STAN) au-
toencoder, which is then converted into a Spiking Neural Network
(SNN) using Brainchip Akida’s Meta TF Software Framework.
This integration of neuromorphic processing aims to enhance
energy efficiency, reduce computational demands, and increase
data transmission rates, optimizing Channel State Information
(CSI) in compliance with 3GPP standards. Our STAN-CNN-
SNN architecture achieves a 85% reduction in computational
requirements, leading to decrease in power consumption, and
increase in data rates within the C-Band spectrum. Simulations
with LEO satellite MSS parameters demonstrate significant
advancements in communication systems. The numerical results
demonstrates substantial computational reductions with minimal
capacity trade-offs.