TY  - JOUR
T1  - Wireless Channel Availability Forecasting with a Sparse Geolocation Spectrum Database by Penalty-Regularization Logistic Models
AU  - Ocampo, Vladimir Iı Christian
AU  - Materum, Lawrence
PY  - 2022
DA  - December
DO  - 10.55549/epstem.1224555
JF  - The Eurasia Proceedings of Science Technology Engineering and Mathematics
JO  - EPSTEM
PB  - ISRES Publishing
WT  - DergiPark
SN  - 2602-3199
SP  - 39
EP  - 45
VL  - 21
LA  - en
AB  - Television uses electromagnetic waves that carry audio and video. The unused frequencies orchannels in broadcasting services are referred to as television white spaces. The unused spectrum can bemanaged to provide internet access in coordination with surrounding TV channels to avoid interference.Different ways of dynamically managing spectrum management have been conceived, and geolocationdatabases are considered the better option. Geolocation databases, when updated and complete, are helpfulwhen frequencies are dynamically shared. In real life, the spectrum availability for a secondary user lacksnumerous information; hence, it is sparse. This paper forecasts wireless channel availability given a sparsegeolocation spectrum database. A dynamic sparse forecasting model is proposed through logistic penalizedregression. Results show that forecasting accuracy is mostly above 90% on average when sparsity penalty termsare incorporated into the model. Forecasting accuracy is improved when penalty terms are integrated into thelogistic regression models to account for sparsity.
KW  - Channel availability
KW  - Forecasting
KW  - Geolocation database
KW  - Penalized logistic regression
KW  - TVWS
UR  - https://doi.org/10.55549/epstem.1224555
L1  - https://dergipark.org.tr/en/download/article-file/2853738
ER  -