Deep Learning and Optimization Approaches in Efficient Resource Management in 6G Communication Networks Using a Hybrid Quantum Duplet-Convolutional Neural Network Model: A Review

Abstract

The evolution of sixth-generation (6G) communication networks introduces significant challenges in resource management due to ultra-high data rates, massive connectivity, and stringent latency requirements. Efficient allocation of resources such as spectrum, power, and bandwidth is essential to maintain Quality of Service (QoS) and energy efficiency in these dynamic environments. Traditional optimization techniques often struggle to cope with the complexity and heterogeneity of 6G systems, leading to the adoption of Artificial Intelligence (AI)-driven approaches. This paper presents a comprehensive review of deep learning and optimization methods for resource management in 6G networks, with a focus on hybrid Quantum Duplet-Convolutional Neural Network (QD-CNN) models. These architectures integrate convolutional neural networks with quantum computing principles to enhance computational efficiency and optimization performance. Techniques such as reinforcement learning for dynamic scheduling, CNN-based allocation strategies, and hybrid CNN-RNN models for network slicing and load balancing are explored. Emerging trends including AI-driven network automation, edge intelligence, and quantum-assisted optimization are also discussed. Despite advancements, challenges such as computational complexity, data limitations, and security concerns remain, highlighting the need for scalable and efficient intelligent solutions.