WiFi Signal Analysis Using Fractional Derivatives

2023-2024 tavasz


Téma leírása

In recent years, WiFi networks have become ubiquitous, playing a crucial role in modern communication systems. Understanding the behavior and characteristics of WiFi signals is essential for optimizing network performance, improving signal reliability, and enhancing user experience. This project proposes to explore the application of fractional derivatives in analyzing WiFi signals to gain deeper insights into their properties and dynamics.



  • Investigate the theoretical foundations of fractional calculus and its relevance to signal analysis.
  • Develop algorithms and methodologies for applying fractional derivatives to WiFi signal analysis.
  • Collect WiFi signal data using appropriate hardware and software tools. (Alternative: using publicly available benchmark databases.)
  • Implement signal processing techniques to preprocess and clean the collected data.
  • Apply fractional derivative-based analysis methods to extract meaningful features and characteristics from WiFi signals.
  • Evaluate the effectiveness and efficiency of the proposed approach compared to traditional signal analysis techniques.
  • Document findings, insights, and recommendations for future research in this domain.




  • Literature Review: Conduct an in-depth review of existing literature on fractional calculus, WiFi signal analysis techniques, and their intersection.
  • Theoretical Understanding: Gain a solid understanding of fractional calculus principles, including fractional derivatives and their applications in signal processing.
  • Data Collection: Utilize WiFi signal capturing tools to collect real-world WiFi signal data in various environments.
  • Preprocessing: Clean and preprocess the collected data to remove noise and artifacts, ensuring data quality.
  • Fractional Derivative Analysis: Implement algorithms to compute fractional derivatives of WiFi signals and explore their utility in characterizing signal behavior.
  • Validation: Validate the results obtained through fractional derivative analysis against established metrics and benchmarks.


  • Programming experience in MATLAB/Python/C++

Külső partner: Nokia Bell Labs

Maximális létszám: 1 fő