Parametric Density Estimation Using EM Algorithm for Collaborative Spectrum Sensing

Autor:	Shun-Te Tseng, James S. Lehnert, Han-Ting Chiang
Rok vydání:	2008
Předmět:	Mathematical optimization Estimation theory Computer science Likelihood-ratio test Expectation–maximization algorithm Density estimation Sensitivity (control systems) Maximization Likelihood function Algorithm Parametric statistics
Zdroj:	CrownCom
DOI:	10.1109/crowncom.2008.4562449
Popis:	Collaborative sensing of spectral occupancy can increase accuracy and relax the required sensitivity of individual sensing units. Collaborative sensing requires knowledge about the densities of collected sensing statistics to form the correct decision statistics for the optimum likelihood ratio test. In this paper, a parametric density estimation scheme using the expectation-maximization (EM) algorithm is proposed to estimate the parameters of densities that are drawn from a given family. When the log-likelihood function for the EM algorithm satisfies a certain condition, the maximization procedure is shown to require only a weighted sum of the collected sensing statistics. Numerical examples show that in various scenarios the proposed EM algorithm produces more accurate estimates than the sample average does.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1eed58eaa74311d0409467dc79e2ff8b https://doi.org/10.1109/crowncom.2008.4562449 Zobrazit plný text záznamu