Multi-taper method (MTM) acts as an effective detector of spectrum sensing in Cognitive radio networks. In this paper, an analytical study was proposed in which reliable, simple, and computationally efficient mathematical terms for the mean and variance of the probability density function (PDF) were derived using the MTM technique. The closed-form expressions for the probability of detection and false alarm for the MTM detector were obtained accordingly. The proposed analytical study was evaluated by intensive simulations using MATLAB. Different simulation techniques were proposed to verify the derived analysis. The existence of White Gaussian Noise was assumed. Important aspects of spectrum detection in cognitive radio networks were included such as, receiver operating characteristics, detection rate versus signal to noise ratio (SNR), and the minimum desired sample points for a specific performance. A comparison was completed with the energy detection technique and all of the results suggested that the proposed paradigm is both credible and powerful under all the parameters considered in the simulation.

https://www.beei.org/index.php/EEI/article/download/2229/2018

Verification analysis for the reliable analytical multi-taper detector in next generation network

In the context of cognitive radio (CR) or various military and civilian applications, modulation recognition (MR) is one of the most popular technical processes in the field of communication system recognition, by which the modulation type of the unknown received signal can be identified automatically by estimating one or more parameters of the modulated signal. This paper presents the performance evaluation of the new proposed blind system recognition method using only a particular property of the second-order statistics of the orthogonal frequency division multiplexing (OFDM) modulated signal. The effectiveness of the proposed method is illustrated using the implementation on universal software radio peripheral (USRP) platform. A comparison with computer simulations using MATLAB software is also performed, emphasizing the good performances of the method while the results obtained are close. We show the efficiency and behavior of the proposed method in the context of wireless communication systems based on OFDM modulation (3GPP/LTE, WiMAX, DVBT-2K, IEEE 802.22-1K,IEEE 802.22-2K, IEEE 802.22-4K). The proposed method can detect OF DM signals among other digital signals in a systematic and intelligent way even with low SNR values (when approaching to SNR=-2dB, the decision criteria tends towards 0).

/pdf/performance-evaluation-of-new-blind-ofdm-signal-recognition-2xldbu3yq0.pdf

Performance evaluation of new blind OFDM signal recognition based on properties of the second-order statistics using universal software radio peripheral platform

The sensing methods with multiple receive antennas  in the Cognitive Radio (CR) device, provide a promising solution for reducing the error rates in the detection of the Primary User (PU) signal. The received Signal to Noise Ratio at the CR receiver is enhanced using the diversity combiners. This paper proposes a statistical approach based on minimum Bayes factors and p-Values as diversity combiners in the spectrum sensing scenario. The effect of these statistical measures in sensing the spectrum in a CR environment is investigated. Through extensive Monte Carlo simulations it is shown that this novel statistical approach based on Bayes factors provides a promising solution to combine the test statistics from multiple receiver antennas and can be used as an alternative to the conventional hypothesis testing methods for spectrum sensing. The Bayesian results provide more accurate results when measuring the strength of the evidence against the hypothesis.

/pdf/a-statistical-approach-to-spectrum-sensing-using-bayes-26lxcsqnf1.pdf

A statistical approach to spectrum sensing using bayes factor and p-Values

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A statistical approach to spectrum sensing using bayes factor and p-Values