Improved hard-decision fusion algorithm against SSDF in cognitive radio networks

2015 ◽  
Author(s):  
Ting Peng ◽  
Yang Zheng ◽  
Xingsheng Pang ◽  
Yuebin Chen ◽  
Kaiyuan Liu
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Decision Fusion ◽  
Radio Networks ◽  
Fusion Algorithm ◽  
Hard Decision

scholarly journals Robust Spectrum Sensing via Double-Sided Neighbor Distance Based on Genetic Algorithm in Cognitive Radio Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Noor Gul ◽  
Muhammad Sajjad Khan ◽  
Junsu Kim ◽  
Su Min Kim
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Decision Fusion ◽  
Radio Networks ◽  
Majority Voting ◽  
Soft Decision ◽  
Neighbor Distance ◽  
Hard Decision

In cognitive radio networks (CRNs), secondary users (SUs) can access vacant spectrum licensed to a primary user (PU). Therefore, accurate and timely spectrum sensing is vital for efficient utilization of available spectrum. The sensing result at each SU is unauthentic due to fading, shadowing, and receiver uncertainty problems. Cooperative spectrum sensing (CSS) provides a solution to these problems. In CSS, false sensing reports at the fusion center (FC) received from malicious users (MUs) drastically degrade the performance of cooperation in PU detection. In this paper, we propose a robust spectrum sensing scheme to minimize the effects of false sensing reports by MUs. The proposed scheme focuses on double-sided neighbor distance (DSND) based on genetic algorithm (GA) in order to filter out the MU sensing reports in CSS. The simulation results show that the sensing results are more accurate and reliable for the proposed GA majority-voting hard decision fusion (GAMV-HDF) and GA weighted soft decision fusion (GAW-SDF) compared to conventional equal gain combination soft decision fusion (EGC-SDF), maximum gain combination soft decision fusion (MGC-SDF), and majority-voting hard decision fusion (MV-HDF) schemes in the presence of MUs.

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