Journal of Computer Networks and Communications / 2012 / Article / Tab 1

Research Article

Learning-Based Spectrum Sensing for Cognitive Radio Systems

Table 1

List of notations.


Received signal by user
Transmitted primary signal
Channel gain coefficients
Zero-mean additive white Gaussian noise (AWGN) at user ’s end
Number of users in cognitive network
-dimensional feature vector consisting of features extracted from different CRs
Number of samples observed to make a decision (observation window size)
Cyclic autocorrelation function
Spectral correlation density function
Cross-correlation between the received signal and the preamble sequence
-dimensional vector consisting of the monomials of a another vector
The classifier model parameters for class
matrix, where is the dimensionality of the input feature vectors (provided by CR users)
and is the number of feature vectors used in the training process
A matrix representing the polynomial expansion of elements in training data set
An ideal target vector representing the ideal channel state (ON or OFF)
Binary global decision on channel availability

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