Performing Hybrid Spectrum Sensing with an Adaptive and Attentive Multi-stacked Deep Learning Network in a Cognitive Radio Network

Cognitive Radio Network (CRN) includes Secondary Users (SUs) and Primary Users (PUs) to perform better communication. The SUs present in the CRN observe the spectrum band to obtain the white space opportunistically. Employing the white spaces supports enriches the effectiveness of the spectrum. Due to the promising learning capacity of Deep Learning (DL) and Machine Learning (ML) models, various experiments in the previous years have utilised the deep or shallow multi-layer perceptron mechanism. However, these mechanisms do not apply to the time series data because of the memory element’s absence. One of the primary issues in spectrum sensing is to model the test statistic. Conventional mechanisms normally employ the model-aided attributes as a test statistic, including eigenvalues and energies. However, these attributes cannot be precisely characterised in the real world. Hence, a DL-assisted hybrid spectrum sensing technique in the CRN is implemented. At first, the data are gathered from appropriate databases. Further, an Adaptive and Attentive Multi-stacked Network (AAMNet) is developed for the hybrid spectrum sensing process. The AAMNet is developed by combining three different deep networks such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and autoencoder. The spectrum sensing process by the proposed AAMNet is enhanced further using the Random parameter Improved Duck Swarm Algorithm (RIDSA) for parameter optimisation. The availability of spectrum is identified for better spectrum utilisation with the help of the developed hybrid spectrum sensing process. Throughout, the analysis of the proposed method is checked by evaluating the resultant outcomes with various heuristic approaches and deep learning methods.