Wireless coverage and frequency availability forecasting with sparse geolocation spectrum databases

Date of Publication

4-2023

Document Type

Master's Thesis

Degree Name

Master of Science in Electronics and Communications Engineering

Subject Categories

Electrical and Electronics | Systems and Communications

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Thesis Advisor

Lawrence Y. Materum

Defense Panel Chair

Melchizedek I. Alipio

Defense Panel Member

Jay Robert B. Del Rosario
Fritz Kevin S. Flores

Abstract/Summary

Television white spaces refer to the unused frequencies or channels in broadcasting services. The unused spectrum can be managed to provide internet access in coordination with surrounding TV channels to avoid interference. 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, sparse. This paper aims to forecast wireless coverage and frequency availability in sparse geolocation spectrum databases. Logistic and vector autoregression models were proposed as dynamic sparse forecasting models. Results show that the logistic models had a decent accuracy of at least 84%. In conjunction with thresholding, the linear VAR models have a decent accuracy with some exceptions, such as time predictions.

Abstract Format

html

Language

English

Format

Electronic

Keywords

Television frequency allocation; Satellite interference geolocation technology; Frequency spectra

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Embargo Period

4-24-2023

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