An Improved Spectrum Sensing Method: Energy-Autocorrelation-Based Detection Technology

Sensing nodes selection and data fusion in cooperative spectrum sensing

“…where P d,i and P fa,i are detection probability and false alarm of local detection for the ith sensing node, respectively, which mathematical expressions are determined by the local sensing algorithms. For example, if we select energy-autocorrelation-based detection [15], P fa,i and P d,i have the expressions as follows…”

“…L is the smooth factor and can be set arbitrarily, which is one of the factors affecting the value of the local detection probability, for details, seen in [15]. η is the local sensing threshold, which must meet the CRLB(h 2…”

“…Energy detection is a blind detection algorithm; however, noise variance must be estimated firstly [5][6][7]. Other detection algorithms, for example, cyclostationarity feature detection [8][9][10], covariance-based absolute value detection [11], eigenvalue detection [12,13], autocorrelation-based detection [14], energy-autocorrelationbased detection [15] etc., have been presented in the published literatures. However some channel parameters of above-mentioned algorithms, for example, channel gain, signal-to-noise ratio (SNR) and noise variance, must be estimated before the algorithms can be applied.…”

Cognitive Radios: Discriminant Analysis for Automatic Signal Detection in Measured Power Spectra

“…Hence, it is very vulnerable to noise uncertainty. Some improvement can be achieved by means of energy and autocorrelation statistics, where no knowledge of the signal and noise are required but an increment on the channel gain is needed [5]. To overcome these shortcomings, test-statistic based methods: the Covariance (CV) method [6], [7] and the Maximum-Minimum Eigenvalue (MME) detection [12] are blind algorithms insensitive to noise.…”

“…Most of these methods are parametric and assume a statistical model of the signal [5]- [9], [14]- [17]. The multitaper method (MTM) is one nonparametric spectral estimator that uses an orthonormal sequence of Slepian tapers or windows and their corresponding eigenspectra values.…”