Intelligent Radio Spectrum Management and Dynamic Spectrum Allocation Algorithm

With the surge in wireless communication services, spectrum resources have grown scarce, posing a significant hindrance to the industry’s expansion. Traditional static spectrum allocation falls short in addressing the escalating demands from communication terminals, particularly under conditions of fluctuating network loads and intricate communication environments. Consequently, exploring intelligent radio spectrum management and dynamic allocation algorithms carries substantial practical importance and application potential. This paper introduces an intelligent radio spectrum management and dynamic allocation approach leveraging deep learning (DL). This approach utilizes a DL model to perceive and forecast the wireless environment in real-time, enabling dynamic adjustments to spectrum resource allocation based on real-time factors like network load and communication quality. By analyzing vast historical data, the DL model identifies patterns and trends in spectrum usage, thereby facilitating precise decision-making for allocation. Experimental results indicate that, in contrast to conventional static methods, this algorithm exhibits greater flexibility and adaptability, effectively navigating complex, evolving wireless landscapes and diverse communication requirements.