Improved Convolutional Neural Network Based Cooperative Spectrum Sensing For Cognitive Radio

• Published in: KSII transactions on Internet and information systems 2021-06, Vol.15 (6), p.2128-2147
• Main Authors: Uppala, Appala Raju, Narasimhulu C, Venkata, Prasad K, Satya
• Format: Article
• Language: Korean
• Subjects: Cognitive radio; Cooperative spectrum sensing; Neural network; Primary user; Simulated annealing
• ISSN: 1976-7277; 1976-7277

Cognitive radio systems are being implemented recently to tackle spectrum underutilization problems and aid efficient data traffic. Spectrum sensing is the crucial step in cognitive applications in which cognitive user detects the presence of primary user (PU) in a particular channel thereby switching to another channel for continuous transmission. In cognitive radio systems, the capacity to precisely identify the primary user’s signal is essential to secondary user so as to use idle licensed spectrum. Based on the inherent capability, a new spectrum sensing technique is proposed in this paper to identify all types of primary user signals in a cognitive radio condition. Hence, a spectrum sensing algorithm using improved convolutional neural network and long short-term memory (CNN-LSTM) is presented. The principle used in our approach is simulated annealing that discovers reasonable number of neurons for each layer of a completely associated deep neural network to tackle the streamlining issue. The probability of detection is considered as the determining parameter to find the efficiency of the proposed algorithm. Experiments are carried under different signal to noise ratio to indicate better performance of the proposed algorithm. The PU signal will have an associated modulation format and hence identifying the presence of a modulation format itself establishes the presence of PU signal.