SpikingRx: From Neural to Spiking Receiver

TL;DR

SpikingRx introduces an energy-efficient neuromorphic receiver using a deep convolutional Spiking Neural Network for 5G signals, achieving comparable performance to traditional neural networks with significantly reduced energy use.

Contribution

The paper presents a novel Spiking Neural Network-based receiver for 5G, demonstrating improved energy efficiency and robustness, with a focus on training methods and interpretability.

Findings

Achieves significant block error rate improvements over conventional 5G receivers.

Consumes approximately 9 times less energy than traditional neural network-based receivers.

Maintains similar performance levels to conventional neural networks.

Abstract

In this work, we propose an energy efficient neuromorphic receiver to replace multiple signal-processing blocks at the receiver by a Spiking Neural Network (SNN) based module, called SpikingRx. We propose a deep convolutional SNN with spike-element-wise ResNet layers which takes a whole OFDM grid compliant with 5G specifications and provides soft outputs for decoded bits that can be used as log-likelihood ratios. We propose to employ the surrogate gradient descent method for training the SpikingRx and focus on its generalizability and robustness to quantization. Moreover, the interpretability of the proposed SpikingRx is studied by a comprehensive ablation study. Our extensive numerical simulations show that SpikingRx is capable of achieving significant block error rate performance gain compared to conventional 5G receivers and similar performance compared to its traditional NN-based counterparts with approximately 9x less energy consumption.