# Analysis of Spectrum Occupancy Using Machine Learning Algorithms

arXiv: https://arxiv.org/abs/1503.07104 · HTML: https://mlsanity.com/paper/1503.07104/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2015.

## Built on by (most cited first, 1 of 1)

- [SCAR: State-Space Compression for Scalable AI-Based Network Management of Vehicular Services](https://mlsanity.com/paper/2508.06243/) (2025)

## Abstract (arXiv)

In this paper, we analyze the spectrum occupancy using different machine learning techniques. Both supervised techniques (naive Bayesian classifier (NBC), decision trees (DT), support vector machine (SVM), linear regression (LR)) and unsupervised algorithm (hidden markov model (HMM)) are studied to find the best technique with the highest classification accuracy (CA). A detailed comparison of the supervised and unsupervised algorithms in terms of the computational time and classification accuracy is performed. The classified occupancy status is further utilized to evaluate the probability of secondary user outage for the future time slots, which can be used by system designers to define spectrum allocation and spectrum sharing policies. Numerical results show that SVM is the best algorithm among all the supervised and unsupervised classifiers. Based on this, we proposed a new SVM algorithm by combining it with fire fly algorithm (FFA), which is shown to outperform all other algorithms.