Determining Pseudo-Random Chaotic Sequences Complexity Using Refined Fuzzy Entropy

The secure transmission of communication systems is a current hot topic of research both domestically and internationally. Within this context, the communication transmission scheme based on dynamic spectrum control emerges as a secure transmission approach suited for contemporary complex dynamic scenarios. The dynamic spectrum control sequence family, serving as the linchpin of this scheme, has assumed a crucial role in safeguarding the security of communication systems. Pseudo-random chaotic sequences as the most commonly used sequences have universality in analyzing the complexity of sequences. While established complexity metrics are commonly employed for accurate calculation and analysis, errors persist when observing sequences over short periods. To address this, the paper proposes a new complexity metric to evaluate the unpredictability of pseudo-random chaotic sequences based on refined fuzzy entropy. Simulation and analysis results demonstrate that R-FuzzyEn effectively characterizes the complexity of various pseudo-random chaotic sequences, outperforming existing algorithms.