Implementation and Analysis of Various Spectrum Sensing Techniques

In this article, we provide detailed implementations and comparative analysis of various spectrum sensing techniques. The content covers fundamental approaches including energy detection, matched filtering, and cyclostationary feature detection algorithms, discussing their core implementation logic through MATLAB/Python code snippets. Each technique is examined with its practical advantages and limitations, supported by key function descriptions such as threshold calculation methods and statistical decision mechanisms. Beyond existing methodologies, we explore current research advancements in cooperative sensing and machine learning-based approaches, along with future development directions in cognitive radio networks. The implementation process for each technique is broken down into algorithmic steps, analyzing application scenarios and performance metrics under different signal-to-noise ratio conditions and primary user characteristics. Readers will gain comprehensive understanding of spectrum sensing aspects, including practical code implementation considerations and performance trade-offs, providing valuable insights for future research and real-world applications in dynamic spectrum access systems.