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Dynamic Spectrum Access Algorithms Based on Survival Analysis

Published

Author(s)

Timothy Hall, Anirudha Sahoo, Robert C. Hagwood, Sarah B. Streett

Abstract

In this study, we design and implement two algorithms for dynamic spectrum access (DSA) that are based on survival analysis. They use a non-parametric estimate of the cumulative hazard function to predict the remaining idle time available for secondary transmission subject to the constraint of a preset probability of successful completion. In addition to theoretical performance analysis of the algorithms, we evaluate them using data collected from a Long Term Evolution (LTE) band to model primary user activity to demonstrate their effectiveness in real-world scenarios, even at fine time scales. The algorithms are run in different configurations, i.e., they are trained and run on a few combinations of data sets. Our results show that as long as the cumulative hazard functions are fairly similar across datasets, the algorithms can be trained on one day's dataset and run on that of another day's without any significant degradation of performance. The algorithms achieve fairly high white space utilization and have a measured probability of interference that always stays below the preset threshold.
Citation
IEEE Transactions on Cognitive Communications and Networking
Volume
3
Issue
4

Keywords

Dynamic spectrum access, spectrum sharing, survival analysis, hazard function

Citation

Hall, T. , Sahoo, A. , Hagwood, R. and Streett, S. (2017), Dynamic Spectrum Access Algorithms Based on Survival Analysis, IEEE Transactions on Cognitive Communications and Networking, [online], https://doi.org/10.1109/TCCN.2017.2771761 (Accessed April 18, 2024)
Created December 20, 2017, Updated November 10, 2018