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Paper Details

Paper Title
Spectrum sensing of OFDM system is improved by using GLRT algorithm
The spectrum sensing of OFDM signals and its practical concerns for cognitive radios (CRs) remain vital and difficult topics. This work presents are placement theme for detecting OFDM signals based on the Neyman-Pearson (NP) principle. In distinction to conventional approaches during which of the log-likelihood operate (LLF) of the samples, that is often used for estimating unknown parameters, and therefore the LLR of an energy detector (ED). These results give insight into the NP detector and also additive white Gaussian noise (AWGN) channels area unit considered or empirical second order statistics based on correlation coefficients area unit utilized, to improve the detection performance, the proposed approach involves considering multipath attenuation channels and also the classical NP detector. The log likelihood ratio (LLR) check is formulated without requiring further pilot symbols by exploitation the redundancy of the cyclic prefix (CP). Analytical results indicate that the LLR of received samples is that the total the relationship between the NP detectors, a detector supported the LLF, and the ED As a result of several unknown parameters should be calculable within the NP detector, two practical generalized log likelihood ratio test (GLRT) detectors area unit designed. To develop a channel-independent GLRT (CI-GLRT) that is crucial for achieving favorable performance over multipath attenuation channels, the complementary property of the coefficient of correlation is employed to derive an estimate freelance of multipath channel profiles. Simulation results ensure the benefits of the proposed detector compared with progressive detectors
spectrum sensing, GLRT algorithm, Neyman-Pearson (NP), cyclic prefix (CP).
Others Details
Paper Id : 94036
Author Name : J BHEEMA REDDY
Co-Author Name(s) : K APARNA
Volume/Issue No : Volume 04 Issue 10
Page No : 143-148
DOI Number : DOI:10.21090/IJAERD.94036
License : This work is licensed under a Creative Commons Attribution 4.0 International License.
website :
Impact Factor : 5.71, SJIF-2017
ISSN Details : eISSN: 2348-4470, pISSN:2348-6406
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