Fast Two-Step Energy Detection for Spectrum Sensing

Abstract

Spectrum sensing is one of the key tasks in cognitive radio. This paper proposes a fast two-step energy detection (FED) algorithm for spectrum sensing via improving the sampling process of conventional energy detection (CED). The algorithm adaptively selectsN-point or 2N-point sampling by comparing its observed energy with prefixed double thresholds, and thereby is superior in sampling time and detection speed. Moreover, under the constraint of constant false alarm, this paper optimizes the thresholds from maximizing detection probability point of view. Theoretical analyses and simulation results show that, compared with CED, the proposed FED can achieve significant gain in detection speed at the expense of slight accuracy loss. Specifically, within high signal-to-noise ratio regions, as much as 25% of samples can be reduced.

An optimal and adaptive double threshold-based approach to minimize error probability for spectrum sensing at low SNR regime

“…3B. For simulation purpose, the normalized threshold is taken as λ* = λ/Ns, i.e., the threshold is normalized by the number of samples (Lai et al 2015 ). The Receiver Operator Characteristic curves were obtained at different values of 0.95 ≤ λ* ≤ 1.50 for SNR = − 10 dB and – 5 dB when N = 2000.…”

“…The authors in Sobron et al ( 2015 ), have presented the use of a cost function dependent on a single parameter which conveys the overall information about the availability of primary users. Another paper proposes a fast two step energy detection-based detection by improving the sampling process of conventional energy detection and further optimizes them (Lai et al 2015 ). Some methods further emphasize on the “no decision region” or the “sensing failure” scenario in a double threshold-based sensing.…”

“…In presence of uncertainty in noise and at low SNR the threshold selection becomes more of an optimization problem. According to Lai et al ( 2015 ), in this paper minimization of error probability is taken as the objective function to derive the optimal threshold. The detection parameters of Eqs.…”

Performance Comparison of Energy Detection Based non – Cooperative Spectrum Sensing Techniques in Cognitive Radio

“…A single threshold energy detection algorithm (STEDA) has been proposed when noise is uncertain and the signal is unknown deterministic [16]. To increase the performance of the detector, a double threshold energy detection algorithm (DTEDA) has been reported by [17], [18] where two other parameters: collision probability and spectrum unavailable probability were numerically calculated and illustrated that collision probability decreases from 32.31% to 8.914% and spectrum unavailable probability increases from 2.331% to 3.57% at a threshold voltage of 10 dB. This finding suggests that the configuration reduces cognitive radio and primary user interference while compromising a little amount of spectrum efficiency.…”