Differential Evolution Based Machine Learning Scheme for Secure Cooperative Spectrum Sensing System

Noor Gul,Su Min Kim,Junsu Kim,Saeed Ahmed,Muhammad Sajjad Khan

doi:10.3390/electronics10141687

Abstract

The secondary users (SUs) in cognitive radio networks (CRNs) can obtain reliable spectrum sensing information of the primary user (PU) channel using cooperative spectrum sensing (CSS). Multiple SUs share their sensing observations in the CSS system to tackle fading and shadowing conditions. The presence of malicious users (MUs) may pose threats to the performance of CSS due to the reporting of falsified sensing data to the fusion center (FC). Different categories of MUs, such as always yes, always no, always opposite, and random opposite, are widely investigated by researchers. To this end, this paper proposes a hybrid boosted tree algorithm (HBTA)-based solution that combines the differential evolution (DE) and boosted tree algorithm (BTA) to mitigate the effects of MUs in the CSS systems, leading to reliable sensing results. An optimized threshold and coefficient vector, determined against the SUs employing DE, is utilized to train the BTA. The BTA is a robust ensembling machine learning (ML) technique gaining attention in spectrum sensing operations. To show the effectiveness of the proposed scheme, extensive simulations are performed at different levels of signal-to-noise-ratios (SNRs) and with different sensing samples, iteration levels, and population sizes. The simulation results show that more reliable spectrum decisions can be achieved compared to the individual utilization of DE and BTA schemes. Furthermore, the obtained results show the minimum sensing error to be exhibited by the proposed HBTA employing a DE-based solution to train the BTA. Additionally, the proposed scheme is compared with several other CSS schemes such as simple DE, simple BTA, maximum gain combination (MGC), particle swarm optimization (PSO), genetic algorithm (GA), and K-nearest neighbor (KNN) algorithm-based soft decision fusion (SDF) schemes to validate its effectiveness.

Highlights

The exponential growth in wireless communication devices and the demands of high data rates require the development of new techniques to meet user and spectrum requirements
hybrid boosted tree algorithm (HBTA) results in an optimum coefficient vector that assists the fusion center (FC) in dealing with all the secondary users (SUs) according to their sensing notifications; One of the significant contributions of the proposed HBTA scheme is that it is trained based on the solutions obtained through differential evolution (DE) and not directly from the SUs, contrary to [44], where direct sensing reports by SUs are employed to train the simple boosted tree algorithm (BTA)
The population size varied in the range of 20 to 80, while the sensing samples changed from 270 to 335

Summary

Introduction

The exponential growth in wireless communication devices and the demands of high data rates require the development of new techniques to meet user and spectrum requirements. A recursive updating algorithm is proposed in [21] that helps in the selection of the SUs with a higher sensing reputation and reduces the impact of the MUs. The scheme presented in [22] allows honest SUs to recommend decisions to the FC about a PU as final along with their local sensing reports to guarantee the reliability of CSS. HBTA results in an optimum coefficient vector that assists the FC in dealing with all the SUs according to their sensing notifications; One of the significant contributions of the proposed HBTA scheme is that it is trained based on the solutions obtained through DE and not directly from the SUs, contrary to [44], where direct sensing reports by SUs are employed to train the simple BTA.

System Model

Proposed Hybrid Boosted Tree Algorithm

Differential Evolution-Based Solution

Boosted Tree Algorithm

Simulation Results

Conclusions and Future Work