Exploiting LTE white space using dynamic spectrum access algorithms based on survival analysis

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. To show that the algorithms are effective in real-world scenarios even at fine time scales, we evaluate their performance using data collected from an LTE band to model primary user activity. 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 which always stays below the preset threshold.