scholarly journals Cooperative Spectrum Sensing using Rule based Hard Decision and Soft Decision with Bayesian Optimized Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Sajjad Khan ◽  
Liaqat Khan ◽  
Noor Gul ◽  
Muhammad Amir ◽  
Junsu Kim ◽  
...  

Cognitive radio is an intelligent radio network that has advancement over traditional radio. The difference between the traditional radio and the cognitive radio is that all the unused frequency spectrum can be utilized to the best of available resources in the cognitive radio unlike the traditional radio. The core technology of cognitive radio is spectrum sensing, in which secondary users (SUs) opportunistically access the spectrum while avoiding interference to primary user (PU) channels. Various aspects of the spectrum sensing have been studied from the perspective of cognitive radio. Cooperative spectrum sensing (CSS) technique provides a promising performance, compared with individual sensing techniques. However, the existence of malicious users (MUs) highly degrades the performance of cognitive radio network (CRN) by sending falsified results to a fusion center (FC). In this paper, we propose a machine learning algorithm based on support vector machine (SVM) to classify legitimate SUs and MUs in the CRN. The proposed SVM-based algorithm is used for both classification and regression. It clearly classifies legitimate SUs and MUs by drawing a hyperplane on the base of maximal margin. After successful classification, the sensing results from the legitimate SUs are combined at the FC by utilizing Dempster-Shafer (DS) evidence theory. The effectiveness of the proposed SVM-based classification algorithm is demonstrated through simulations, compared with existing schemes.


2021 ◽  
Author(s):  
venkateshkumar Udayamoorthy ◽  
Ramakrishnan Sriniva

Abstract In this paper, a cooperative spectrum sensing (CSS) model is proposed to sense n-number of primary users (PUs) using n-number secondary users (SUs) in a sequence by applying support vector machine (SVM) algorithm using three different kernels namely linear, polynomial and radial basis function (RBF) respectively. In this method, fusion centre (FC) instructs all the SUs through control channel, which PU is to be sensed by sending a pre-defined primary user identification code (PUid) and each SU sense the Kth PU spectrum information and stored in a database at FC. SU transmits a bit ‘0’ or bit ‘1’ along with PU sensing information to the FC to indicate whether it needs a spectrum band to transmit the data or not. SU add two identification codes along with sensing information to the FC which indicates that from which SU the sensing information received and which PU is sensed by the SU. For simulation 500 data samples are used and the simulation results show an accuracy of 96% and false alarm value of 1.3% in classifying the SU sensing information at FC using RBF kernel. Another method is proposed with multiclass classification by applying SVM algorithm using RBF kernel. The confusion region class is classified with zero false alarm percentage and achieves an accuracy of 99.3% in classifying the SU sensing information at FC.


Sign in / Sign up

Export Citation Format

Share Document