Dynamic Channel Allocation Algorithms and Spectrum Resource Management Optimization in 6G Networks

The emergence of sixth-generation (6G) wireless networks represents a paradigm shift in wireless communications, introducing unprecedented capabilities in spectrum utilization, latency reduction, and data transmission rates. This research examines advanced spectrum resource optimization and dynamic channel allocation techniques specifically designed for 6G's unique characteristics and demands. Through theoretical analysis and simulation studies, we investigate three novel frameworks: Quantum-Enhanced Proportional Fairness (QPF) for multi-dimensional resource allocation, AI-driven Adaptive Modulation and Coding (AI-AMC) leveraging deep reinforcement learning, and Holographic Network Slicing (HNS) for seamless integration of physical and virtual network resources. Our findings demonstrate significant improvements in spectrum efficiency compared to conventional 5G methodologies, while supporting emerging applications such as holographic communications and digital twins. We present a comprehensive mathematical framework for optimizing channel allocation in ultra-dense networks operating at terahertz frequencies, validated through extensive quantum computer simulations. The research advances both the theoretical foundation for 6G spectrum management and provides practical implementation strategies for future ultra-reliable low-latency communications.