Non-uniform Spectrum Sensing Using Computationally Efficient 2-level (FFT-Goertzel) Based Energy Detection

Energy detection in frequency domain is a preferred technique for the spectrum sensing and the accuracy of frequency estimation depends on the DFT size. A new technique for energy detection is proposed here. Instead of computing full length (N-point) DFT of the whole data, this paper proposes a two level (coarse-fine) approach. In the first (coarse) level, time averaging of smaller size (L<;<;N) data blocks of the whole data and its DFT are computed and Neymen Pearson based detection is performed to determine the presence of energy in the subbands. In the second level (fine), Goertzel algorithm is applied to determine the fine estimates in those subbands. Matlab based experiments were performed to verify the proposed method. Simulation result also shows that this method can be applied for non-uniformly occupied spectrum also. The complexity of this approach is evaluated and it is about 51% computationally more efficient at -5 dB signal to noise ratio of received signal.