Abstract

: Efficient spectrum allocation is critical for modern wireless communication systems due to the exponential growth of connected devices and the scarcity of available spectrum. Traditional static or heuristic-based allocation methods often fail to adapt dynamically to changing network conditions, leading to underutilized spectrum and interference. Reinforcement learning (RL), a branch of artificial intelligence, provides a promising framework for intelligent spectrum allocation by enabling autonomous decision-making in dynamic environments. This study explores the application of RL models, including Q-learning, deep Q-networks, and multi-agent reinforcement learning, for optimizing spectrum usage in wireless networks. The research investigates state representation, reward design, and policy optimization strategies to maximize spectrum efficiency while minimizing interference and latency. A comparative analysis of RL-based allocation techniques with traditional methods is conducted through simulations and prototype implementation. Results demonstrate that RL models can effectively learn optimal allocation strategies, adapt to traffic variations, and improve overall network performance. The study also identifies challenges such as convergence time, computational complexity, and scalability in large-scale networks. These insights provide a roadmap for deploying intelligent, self-learning spectrum management systems in 5G, 6G, and beyond, enhancing wireless communication reliability and spectrum utilization efficiency