Deep Learning and Optimization Approaches in Graph Neural Networks with Optimized Attention Long-Range CNN for Traffic Prediction and Resource Allocation in 6G Wireless Systems: A Review

Abstract

The emergence of 6G wireless systems has introduced unprecedented challenges in managing network traffic and resource allocation due to ultra-dense connectivity, massive data generation, and stringent latency requirements. Accurate traffic prediction and efficient resource allocation are critical to ensuring quality of service (QoS) and energy efficiency in next-generation networks. Recently, deep learning approaches, particularly Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs) with long-range attention mechanisms, have shown significant potential in modelling complex spatio-temporal dependencies in network traffic. This review explores advanced deep learning and optimization techniques for traffic prediction and resource allocation in 6G systems. It focuses on Graph Neural Networks integrated with optimized attention-based long-range CNN architectures to capture both spatial and temporal correlations in network data. Additionally, reinforcement learning and optimization algorithms are analysed for dynamic resource allocation. The study highlights recent advancements, including spatio-temporal GNN models, attention-based architectures, and hybrid optimization frameworks. Results from existing literature indicate that combining GNNs with attention-based CNNs significantly improves prediction accuracy and resource utilization efficiency. Finally, the paper discusses open challenges such as scalability, computational complexity, and real-time deployment, providing insights for future research in intelligent 6G wireless systems.