Cooperative spectrum sensing based hybrid machine learning technique for prediction of secondary user in cognitive radio networks

被引:0
|
作者
Thuraipandi, Sivagurunathan Paramasivam [1 ]
Nagarajan, Sathish Kumar [2 ]
机构
[1] M Kumarasamy Coll Engn, Elect & Commun Engn, Karur, India
[2] Sri Ramakrishna Engn Coll, Elect & Commun Engn, Coimbatore, Tamil Nadu, India
关键词
Energy detector; primary user; cognitive radio network; secondary user; cooperative spectrum sensing; DECISION FUSION; PERFORMANCE; SELECTION; CHANNEL; HOYT; PROBABILITY; HANDOFF;
D O I
10.3233/JIFS-222983
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The spectrum scarcity problem in today's wireless communication network is addressed through the use of a cognitive radio network (CRN). Detection in the spectrum is made easier by cooperative spectrum sensing (CSS), which is a tool developed by the military. The fusion centre receives the sensing information from each secondary user and uses it to make a global conclusion about the presence of the principal user. Literature has offered several different methods for decision making that lack scalability and robustness. CSS censoring is inspected in the attendance of faded settings in the current study. Rayleigh fading, which affects reporting channels (R), is examined in detail. Multiple antennae and an energy detector (ED) are used by each secondary user (SU). A selection combiner (SC) combines the ED outputs with signals from the primary user (PU), which are established by several antennas on SU, before the joint signal is utilised to make a local result. SUs are expurgated at the fusion centre (FC) using a hybrid Support Vector Machine (SVM) that significantly improves detection performance and reduces the number of false positives. With a minimum false alarm probability of 0.1, error rate of 0.04, spectrum utilization of 99%, throughput of 2.9kbps and accuracy of 99%, proposed model attains better performance than standard SVM and Artificial Neural Network (ANN) models.
引用
收藏
页码:3959 / 3971
页数:13
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