Queuing process optimization in software-defined radio: Enhancing system performance and adaptability

Software-Defined Radio (SDR) systems have become pivotal in modern communication, offering unparalleled flexibility and adaptability. This study focuses on the queuing process within SDR architectures, exploring methods to optimize performance and address challenges in real-time signal processing. Queuing theory, traditionally applied in telecommunications and computer science, is adapted to the unique characteristics of SDR, where signals traverse a dynamic processing pipeline. The queuing process in SDR involves stages such as RF front-end reception, digital down conversion, baseband processing, and higher-layer protocol handling. Challenges, including noise, interference, frequency offset, multipath fading, and sampling rate mismatches, can impact the efficiency of the queuing system. The study investigates techniques for dynamic spectrum access, adaptive filtering, and signal reprocessing to mitigate these challenges. By leveraging queuing theory principles, the research aims to optimize resource allocation, reduce latency, and enhance the overall efficiency of the SDR queuing process. This involves dynamic adjustment of queuing parameters, adaptive scheduling algorithms, and the integration of intelligent decision-making mechanisms. Additionally, the study explores the impact of queuing processes on the system's ability to support various communication standards and adapt to changing environmental conditions. The findings from this research contribute to a deeper understanding of the queuing dynamics in SDR systems, providing insights into potential improvements for real-time signal processing. The optimized queuing process enhances the SDR system's responsiveness, adaptability, and reliability, making it well-suited for diverse applications in wireless communication, military operations, and beyond.