Spectrum Sensing of OFDM system is improved by using GLRT Algorithm

Abstract

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.