An inter-frame dynamic double-threshold energy detection for spectrum sensing in cognitive radios

Abstract

This paper proposes an inter-frame dynamic double threshold (IF-DDT) spectrum sensing algorithm in order to improve the sensing performance based on energy detection (ED) in cognitive radios (CRs). Based on both the activity model of the primary user (PU) and the sensing mechanism of the secondary user (SU), the proposed algorithm exploits the relationship between two adjacent sensing frames and designs dynamic double thresholds for each sensing frame to enhance spectrum sensing performance when the received energy cannot give a reliable local decision. The detection probability and false alarm probability of the proposed sensing scheme are analyzed, and an algorithm for searching the optimal dynamic double thresholds is derived with very low complexity according to the Neyman-Pearson (NP) test criterion. Theoretical analysis and simulation results show that the proposed algorithm outperforms the ED algorithm.

Adaptive Double-Threshold Cooperative Spectrum Sensing Algorithm Based on History Energy Detection

“…There are some methods [5,6] that optimized the single threshold and history-based method as [7] named three-event ED (3EED), which decides in one sensing event, considering also the event immediately before and the one immediately after it, which all lead to improving the energy detection performance-based single threshold. Furthermore, to overcome the weakness of single-threshold detection, a lot of double-threshold detection schemes [8][9][10][11][12][13][14][15][16][17][18][19] are proposed to improve the performance of the system to a certain extent. Nowadays, many new methods are continuously proposed for energy detection in spectrum sensing.…”

“…To address this problem, a memory-based energy detection method is presented in [13] by using memory sticks to improve the performance with the cost of delay in secondary devices' communication. Moreover, in [14][15][16], the correlation coefficient of received signals, the two-state Markov chain model, and the historical sensing data are employed, respectively, to determine a result in case the energy lies between the two thresholds. In [17], when the energy is between two thresholds, the result of the previous sensing time is used as the result of this time, and if there is also no result in the previous time, then wait for the result of the next sensing time to update the current time until generating a result.…”