On the Accuracy of Maximum Likelihood Estimation for Primary User Behavior in Cognitive Radio Networks

Author

Xiaoyuan Li ; Dexiang Wang ; Xiang Mao ; McNair, J.

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Florida, Gainesville, FL, USA

Volume

17

Issue

5

fYear

2013

fDate

May-13

Firstpage

888

Lastpage

891

Abstract

The primary user (PU)´s busy/idle behavior in a cognitive radio network is conventionally modeled using a two-state Markov chain. Maximum likelihood (ML) estimation is widely applied to estimate the state transition probabilities. This letter derives a precise expression of the probability mass function (PMF) for the ML estimator, which has not been reported in the literature. By leveraging the exact PMF expression, the essential relation among the number of samples, transition probabilities, and estimation accuracy is revealed.

Keywords

Markov processes; cognitive radio; maximum likelihood estimation; probability; ML estimation; PMF; PMF expression; PU busy-idle behavior; cognitive radio networks; maximum likelihood estimation; primary user busy-idle behavior; probability mass function; state transition probabilities; two-state Markov chain; Accuracy; Channel estimation; Cognitive radio; Markov processes; Maximum likelihood estimation; Standards; Primary user behavior; probability mass function; transition probabilities; two-state Markov chain;

fLanguage

English

Journal_Title

Communications Letters, IEEE

Publisher

ieee

ISSN

1089-7798

Type

jour

DOI

10.1109/LCOMM.2013.031913.122829

Filename

6486525