Abstract
Spectrum sensing technology plays an increasingly important role in cognitive radio networks. Consequently, several spectrum sensing algorithms have been proposed in the literature. In this paper, we present a new spectrum sensing algorithm “Differential Characteristics-Based OFDM (DC-OFDM)” for detecting OFDM signal on account of differential characteristics. We put the primary value on channel gain θ around zero to detect the presence of primary user. Furthermore, utilizing the same method of differential operation, we improve two traditional OFDM sensing algorithms (cyclic prefix and pilot tones detecting algorithms), and propose a “Differential Characteristics-Based Cyclic Prefix (DC-CP)” detector and a “Differential Characteristics-Based Pilot Tones (DC-PT)” detector, respectively. DC-CP detector is based on auto-correlation vector to sense the spectrum, while the DC-PT detector takes the frequency-domain cross-correlation of PT as the test statistic to detect the primary user. Moreover, the distributions of the test statistics of the three proposed methods have been derived. Simulation results illustrate that all of the three proposed methods can achieve good performance under low signal to noise ratio (SNR) with the presence of timing delay. Specifically, the DC-OFDM detector gets the best performance among the presented detectors. Moreover, both of the DC-CP and DC-PT detector achieve significant improvements compared with their corresponding original detectors.
Highlights
With the rapid development of wireless communication applications, the problem of spectrum scarcity has become more serious than ever before [1,2]
Some numerical results of the proposed schemes are given for sensing Orthogonal frequency division multiplexing (OFDM) signals over frequency selective fading channel, and the performance of the algorithms is indicated as the probability of missed-detection (Pm ) via 106 Monte Carlo simulations
Three new spectrum sensing algorithms for OFDM signals are investigated under low signal to noise ratio (SNR) environment with the presence of a timing delay
Summary
With the rapid development of wireless communication applications, the problem of spectrum scarcity has become more serious than ever before [1,2]. Spectrum sensing methods to detect the OFDM signals based on the feature of cyclic prefix and studied the generalized likelihood ratio test (GLRT) according to the second order statistic of the received. In [17], specific detection algorithms (feature match) are presented These proposed sensing methods are robust to frequency offset and noise power uncertainty, they could not perform perfect spectrum sensing for sensing errors. Three novel spectrum sensing algorithms based on derivative features are proposed to detect OFDM signals. (5) We derive the theoretical expressions of Pm and Pf of the three proposed detectors, which are based on the probability distribution functions for the different test statistics under H0 and H1 From these theoretical results, it is easy to get the corresponding thresholds for any given Pf.
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