Abstract
Spectrum sensing is one of the key technologies in the field of cognitive radio, which has been widely studied. Among all the sensing methods, energy detection is the most popular because of its simplicity and no requirement of any prior knowledge of the signal. In the case of low signal-to-noise ratio (SNR), the traditional double-threshold energy detection method employs fixed thresholds and there is no detection result when the energy is between high and low thresholds, which leads to poor detection performance such as lower detection probability and longer spectrum sensing time. To address these problems, we proposed an adaptive double-threshold cooperative spectrum sensing algorithm based on history energy detection. In each sensing period, we calculate the weighting coefficient of thresholds according to the SNR of all cognitive nodes; thus, the upper and lower thresholds can be adjusted adaptively. Furthermore, in a single cognitive node, once the current energy is within the high and low thresholds, we utilize the average energy of history sensing times to rejudge. To ensure the real-time performance, if the average history energy is still between two thresholds, the single-threshold method will be used for the end decision. Finally, the fusion center aggregates the detection results of each node and obtains the final cooperative conclusion through “or” criteria. Theoretical analysis and simulation results show that the algorithm proposed in this paper improved detection performance significantly compared with the other four different double-threshold algorithms.
Highlights
With the popularization of mobile Internet and the Internet of things, the demand for spectrum resources increases dramatically
The main contributions of this paper are as follows: (1) We proposed an adaptive double-threshold cooperative spectrum sensing algorithm based on history energy detection, which excellent performance is verified by simulation especially in the case of low signal-to-noise ratio (SNR)
On the basis of the original weighted coefficient of [11], the algorithm weighted doublethreshold energy detection method (WDT-ED) added the ratio of the current user SNR value to the average SNR value to increase the accuracy of the thresholds
Summary
With the popularization of mobile Internet and the Internet of things, the demand for spectrum resources increases dramatically. (1) We proposed an adaptive double-threshold cooperative spectrum sensing algorithm based on history energy detection, which excellent performance is verified by simulation especially in the case of low SNR (2) Based on the traditional double-threshold detection, plus in the weighted coefficient obtaining from SNR values of SUs, the two decision thresholds are adjusted adaptively to improve the performance of energy detection (3) To ensure the real-time performance, utilizing the average energy of history sensing times to complete subsequent judgment when the detected energy is between the high and the low threshold, and if the average history energy is still between two thresholds, the single-threshold method will be utilized as the end decision to guarantee that there will be at most only two rounds of detection, thereby enhancing the low complexity (4) We derivated the exact closed-form expressions of the detection probability, the false alarm probability, and the missed detection probability base on the proposed scheme, respectively.
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