A Comprehensive Review of Graph Neural Networks with Optimized Attention Long-Range CNN for Traffic Prediction and Resource Allocation in 6G Wireless Systems

Abstract

The rapid evolution of sixth-generation (6G) wireless systems has introduced unprecedented challenges in traffic prediction and resource allocation due to ultra-dense connectivity, heterogeneous networks, and dynamic user behaviour. Accurate traffic prediction is critical for efficient resource allocation, load balancing, and energy optimization in 6G environments. Graph Neural Networks (GNNs) have emerged as a powerful tool for modelling complex spatial-temporal dependencies in wireless networks, while Long-Range Convolutional Neural Networks (CNNs) with attention mechanisms enhance temporal feature extraction and prediction accuracy. Recent studies demonstrate that integrating GNNs with optimized attention-based long-range CNN architectures significantly improves prediction performance and enables proactive resource allocation strategies. For instance, spatial–temporal graph neural networks combined with reinforcement learning have shown improvements in energy efficiency and load balancing by up to 12% in cellular systems. Additionally, AI-driven resource management frameworks have been identified as essential for achieving energy-efficient and intelligent 6G networks. This paper presents a comprehensive review of AI-based traffic prediction and resource allocation techniques using GNN and optimized CNN architectures. It highlights recent advancements, discusses emerging trends, and identifies key challenges such as scalability, data dependency, and computational complexity. Finally, future research directions for intelligent and adaptive 6G systems are outlined.