1. A method for implementing a software defined radio for a Spectrum Sensing Function (SSF) for detecting signals in Gaussian noise, wherein Higher Order Statistics (HOS) are applied to segments of received signals in at least one of time and frequency domains comprising the steps of: providing a receiver device of said software defined radio comprising a processor; at an antenna of said receiver device, receiving said received signal comprising a frequency spectrum; selecting a particular portion of said frequency spectrum; in a band pass filter, excluding regions of said frequency spectrum that are outside of said selected portion; in a low noise amplifier, amplifying output of said band pass filter; adjusting amplifier gain for said amplified output of said band pass filter; in a waveform collector, collecting waveforms from said selected portion of said frequency spectrum; in a downconverter, downconverting said collected waveforms to shift a characteristic frequency component of said collected waveforms to a specified frequency; in an analog to digital converter, applying an analog to digital conversion to said downconverted collected waveforms in an analog to digital converter; applying a low pass filter to filter said downconverted collected waveforms; up or down converting on a spectrum of interest of said downconverted collected waveforms to shift a characteristic frequency component of said downconverted collected waveforms to a specified detection frequency, producing second-converted waveforms; sampling said second-converted waveforms to adjust a sampling rate; applying a serial to parallel converter to convert said sampled second-converted waveforms to a stream of time domain segments, each said time domain segment including a plurality of time domain samples; applying a Fast Fourier Transform (FFT) to each said time domain segment to obtain a corresponding frequency domain segment, each of said frequency domain segments including a plurality of frequency domain samples in a Fast Fourier Transform stage; in said processor, detecting at least one signal by processing said segments using Higher Order Statistics; said processing said segments using Higher Order Statistics comprising dividing said data segments into real and imaginary parts, wherein R is a total number of moments (m r â€” real , m r â€” imaginary ) and cumulants (c r â€” real , c r â€” imaginary ) of order greater than two available for computation for said real and said imaginary parts of each segment of said data segments respectively; choosing a value for a probability step parameter (Î´) between zero and one; setting a Psignal_real and a Psignal_imaginary to 0.5; choosing a value for a fine threshold parameter (Î³) greater than zero, wherein said fine threshold parameter Î³ is used to control a probability of false alarm P FA and a probability of detection P D ; in said processor, computing all R+2 moments and cumulants, wherein for each value of r where r equals 3 to (R plus 2); if |c r â€” real | is less than Î³|m 2 â€” real | r/2 , then P Signal â€” real equals P Signal â€” real minus Î´; if |c r â€” real | is greater than or equal to Î³|m 2 â€” real | r/2 , then P Signal â€” real equals P Signal â€” real plus Î´; and wherein if |c r â€” imaginary | is less than Î³|m 2 â€” imaginary | r/2 , then P Signal â€” imaginary equals P Signal â€” imaginary minus Î´; if |c r â€” imaginary | is greater than or equal to Î³|m 2 â€” imaginary | r/2 , then P Signal â€” imaginary equals P Signal â€” imaginary plus Î´; and P Signal equals aP Signal â€” real plus bP Signal â€” imaginary , wherein a and b are weight parameter coefficients; in a classifier, classifying each segment as belonging to Class Signal or Class Noise; assigning said data sample segment to Class Signal if P signal is greater than or equal to 0.5; and assigning said data sample segment to Class Noise if P signal is less than 0.5 and no signal is detected; and in said processor, identifying said at least one signal.