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Dynamic Spectrum Access Algorithms Based on Survival Analysis.

Timothy A Hall1, Anirudha Sahoo1, Charles Hagwood2

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Summary
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Two new dynamic spectrum access algorithms predict remaining idle time using survival analysis. They achieve high white space utilization with minimal interference, even when trained on different datasets.

Keywords:
Dynamic spectrum accesshazard functionspectrum sharingsurvival analysis

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Area of Science:

  • Wireless communication
  • Signal processing
  • Data science

Background:

  • Dynamic spectrum access (DSA) is crucial for efficient radio spectrum utilization.
  • Traditional DSA methods often struggle with accurately predicting spectrum availability.
  • Survival analysis offers a novel approach to model time-to-event data, applicable to spectrum availability.

Purpose of the Study:

  • To design and implement two novel dynamic spectrum access algorithms.
  • To leverage survival analysis for predicting remaining idle spectrum time.
  • To evaluate algorithm performance in real-world scenarios and assess cross-dataset applicability.

Main Methods:

  • Developed two algorithms based on non-parametric survival analysis (cumulative hazard function).
  • Predicted remaining idle time for secondary transmissions with a specified success probability.
  • Validated algorithms using Long Term Evolution (LTE) band data to model primary user activity.
  • Tested algorithms across different training and testing dataset combinations.

Main Results:

  • Algorithms demonstrated effectiveness in real-world scenarios, even at fine time scales.
  • Performance remained robust when algorithms were trained on one dataset and applied to another, provided similar cumulative hazard functions.
  • Achieved high white space utilization.
  • Measured probability of interference remained at or below the preset threshold.

Conclusions:

  • The proposed survival analysis-based algorithms are effective for dynamic spectrum access.
  • The algorithms offer reliable spectrum availability prediction and efficient spectrum utilization.
  • Cross-dataset training demonstrates the algorithms' adaptability and robustness in dynamic radio environments.