A Two-Step Cooperative Energy Detection Algorithm Robust to Noise Uncertainty

Tingting Yang,Yucheng Wu,Weiyang Xu,Weiqiang Tan,Liang Li

doi:10.1155/2019/3912784

Tingting Yang, Yucheng Wu + Show 3 more

Open Access

https://doi.org/10.1155/2019/3912784

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Abstract

In order to achieve accurate interference detection in complex electromagnetic environments, a two-step cooperative stochastic resonance energy detection (TCSRED) algorithm is proposed to address the problem, where the traditional energy detection (ED) performance is susceptible to noise uncertainty. By combining two thresholds and two-step cooperation, the generalized stochastic resonance is applied to the energy detection, which effectively reduces the complexity and detection time. In particular, when a certain decision result is obtained in the first step of detection, the decision is finished and the second step of detection is unnecessary. Otherwise, the second step of detection is performed to obtain the final decision result. Simulation results show that the proposed algorithm is robust to the noise uncertainty. Even in the case of a low signal-to-noise ratio (SNR), it also performs better than existing methods without significant increment of the complexity.

Highlights

Interference detection has attracted more and more attention in recent years due to its importance to anti-interference processing [1,2,3,4]
ED denotes the traditional energy detection algorithm and GSRED denotes the energy detection algorithm based on generalized stochastic resonance
We provided other algorithms [6, 10], and we observe from Figure 5 that the two-step cooperative stochastic resonance energy detection (TCSRED) algorithm has a significant improvement compared with other algorithms under the same interference-to-noise ratio (INR) and noise uncertainty

Summary

Introduction

Interference detection has attracted more and more attention in recent years due to its importance to anti-interference processing [1,2,3,4]. Energy detection algorithm has proven to be one of the promising solutions for signal detection by virtue of its simplicity, ease of implementation, and availability. Energy detection provides key benefits in terms of signal detection, readily achievable in practical application scenario without any priori information [6, 7]. Like any other interference detection algorithms, energy detection is facing challenges due to its sensitivity to noise uncertainty and the increasing demands for better detection performance and shorter detection time. Owing to the fact that proper selection of decision threshold to ensure the detection performance is very difficult at low signal-to-noise ratio (SNR), accurate estimate of noise variance is nearly impossible due to the existence of noise uncertainty

Results

Discussion

Conclusion