@article{article_1224555, title={Wireless Channel Availability Forecasting with a Sparse Geolocation Spectrum Database by Penalty-Regularization Logistic Models}, journal={The Eurasia Proceedings of Science Technology Engineering and Mathematics}, volume={21}, pages={39–45}, year={2022}, DOI={10.55549/epstem.1224555}, author={Ocampo, Vladimir Iı Christian and Materum, Lawrence}, keywords={Channel availability, Forecasting, Geolocation database, Penalized logistic regression, TVWS}, abstract={Television uses electromagnetic waves that carry audio and video. The unused frequencies or
channels in broadcasting services are referred to as television white spaces. The unused spectrum can be
managed to provide internet access in coordination with surrounding TV channels to avoid interference.
Different ways of dynamically managing spectrum management have been conceived, and geolocation
databases are considered the better option. Geolocation databases, when updated and complete, are helpful
when frequencies are dynamically shared. In real life, the spectrum availability for a secondary user lacks
numerous information; hence, it is sparse. This paper forecasts wireless channel availability given a sparse
geolocation spectrum database. A dynamic sparse forecasting model is proposed through logistic penalized
regression. Results show that forecasting accuracy is mostly above 90% on average when sparsity penalty terms
are incorporated into the model. Forecasting accuracy is improved when penalty terms are integrated into the
logistic regression models to account for sparsity.}, publisher={ISRES Publishing}