Spectrum sensing using envelope tracking and signal moment

Spectrum sensing is one of the major component of the cognitive radio architecture. Energy Detection (ED) and Covariance Absolute Value (CAV) are well known algorithms for spectrum sensing. However, CAV is efficient for sensing correlated signals whereas ED suffers from SNR Wall limitation. In this paper, we propose an M4-Edge algorithm to overcome the limitations of CAV and ED algorithms. The proposed algorithm tracks the envelope of the signal burst, of the primary user, in the time domain. The fourth central moment of the envelope is evaluated and compared with a threshold to detect the rising and falling edges of the burst and hence detecting the presence of signal. Further, the algorithm is implemented on a Xilinx Vertex 6 Field Programmable Gate Array development board for evaluating its real time performance. In the real time, the performance of the proposed algorithm is compared with ED and CAV algorithm by considering both BFSK and DVBT signal corrupted by Additive White Gaussian Noise (AWGN) and flat fading, as the Primary user signal to be sensed. The probability of detection, sensing time and resource utilisation are used as the metrics for measuring the efficiency of the algorithms. The sensing time for all three algorithms vary between 2 to 4 milliseconds within their SNR capability envelopes. The FPGA resource utilization is lowest for ED and highest for M4-Edge algorithm. The proposed algorithm outperforms ED and has equivalent performance to CAV in Signal-to-Noise-Ratio (SNR) range of -12 to +10 dB. The proposed algorithm has the additional benefit that it performs well when the signal is not correlated. The experimental results reveal that, in the case of the (Digital Video Broadcast Terrestrial) DVBT signal, the proposed M4-Edge algorithm detects the signal with probability of detection 0.9 at -12 dB SNR, whereas CAV algorithm fails to detect, due to lack of correlation in the signal.