Artificial Intelligence Techniques for Graph Neural Networks with Optimized Attention Long-Range CNN for Traffic Prediction and Resource Allocation in 6G Wireless Systems: Trends and Challenges

Abstract

The evolution of sixth-generation (6G) wireless communication systems demands intelligent, adaptive, and highly efficient mechanisms for traffic prediction and resource allocation. Artificial Intelligence (AI), particularly deep learning models such as Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs), has emerged as a promising solution for handling complex spatio-temporal dependencies in network traffic. GNNs effectively model non-Euclidean data structures inherent in communication networks, while attention mechanisms enhance their ability to capture dynamic relationships among nodes. Additionally, long-range CNN architectures facilitate the extraction of hierarchical temporal features, improving prediction accuracy over extended time horizons. Recent research highlights that integrating optimized attention mechanisms within GNNs significantly improves traffic prediction by adaptively weighting node relationships. These models are further enhanced by hybrid frameworks combining GNNs, CNNs, and recurrent architectures to address latency, scalability, and real-time processing challenges in 6G networks. Accurate traffic prediction plays a crucial role in enabling efficient resource allocation, network slicing, and quality of service (QoS) management. Despite these advancements, several challenges remain, including model complexity, data heterogeneity, scalability, and energy efficiency. This paper reviews recent AI techniques, identifies key research trends, and discusses future directions for intelligent traffic prediction and resource optimization in 6G wireless systems.