AI-Enabled Dynamic Spectrum Management for 6G Networks: A Comprehensive Framework and Performance Analysis
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AI-Enabled Dynamic Spectrum Management for 6G Networks:
A Comprehensive Framework and Performance Analysis
Authors:
Arjoo 1, Dr. Amandeep2, Kirti3, Varsha4
M.Sc. Computer Science 1, 3, 4, Artificial Intelligence and Data Science, GJU S&T Hisar
Assistant Professor 2, Artificial Intelligence and Data Science, GJU S&T Hisar
Email- aarjoomalik1@gmail.com
Abstract
In this paper, I provide a complete review on the application of Artificial Intelligence (AI) technologies into Dynamic Spectrum Management (DSM) for sixth-generation (6G) wireless networks. The complexity, device population, and QoS requirements for 6G are far beyond the scope of traditional spectrum allocation models. A novel multi-level architecture of AI-DSM based on supervised, reinforcement, and federated learning is proposed in this work. Through case studies, simulation tools, and defined KPIs, we assess the performance, scalability, and fairness of AI-DSM systems. I conclude this paper by discussing the challenges of security, alignment with policies, and ethical oversight.
Keywords:
6G Networks, Dynamic Spectrum Management (DSM), Artificial Intelligence (AI), Reinforcement Learning, Federated Learning, Cognitive Radio, Spectrum Efficiency, UAV Communications, Smart Grid, Edge Intelligence, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), AI-native Protocols, Spectrum Sharing, Security and Privacy in AI, Explainable AI, Real-time Wireless Systems.
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