Lightweight Graph Neural Networks for Enhanced 5G NR Channel Estimation

TL;DR

This paper presents GraphNet, a lightweight Graph Neural Network for 5G NR channel estimation, offering improved accuracy in dynamic environments with reduced computational complexity suitable for edge deployment.

Contribution

Introduces GraphNet, a novel GNN-based channel estimator that outperforms existing methods in dynamic scenarios while maintaining low computational overhead.

Findings

GraphNet matches ChannelNet in stable conditions.

GraphNet significantly outperforms in high-variation environments.

Includes built-in noise estimation for robustness.

Abstract

Effective channel estimation CE is critical for optimizing the performance of 5G New Radio NR systems particularly in dynamic environments where traditional methods struggle with complexity and adaptability This paper introduces GraphNet a novel lightweight Graph Neural Network GNNbased estimator designed to enhance CE in 5G NR Our proposed method utilizes a GNN architecture that minimizes computational overhead while capturing essential features necessary for accurate CE We evaluate GraphNet across various channel conditions from slowvarying to highly dynamic environments and compare its performance to ChannelNet a wellknown deep learningbased CE method GraphNet not only matches ChannelNets performance in stable conditions but significantly outperforms it in highvariation scenarios particularly in terms of Block Error Rate It also includes builtin noise estimation that enhances robustness in challenging channel conditions Furthermore its significantly lighter computational footprint makes GraphNet highly suitable for realtime deployment especially on edge devices with limited computational resources By underscoring the potential of GNNs to transform CE processes GraphNet offers a scalable and robust solution that aligns with the evolving demands of 5G technologies highlighting its efficiency and performance as a nextgeneration solution for wireless communication systems