Intelligent Cognitive Internet of Things -based Spectrum Sensing Algorithm for Future Communication
Mohit Kumar Bhadla, Pankaj Kumar, Mukesh Soni, Ismail Mohamed Keshta, Aadam Quraishi, Mahmood Abdulrazzaq Alsaadi, Haewon Byeon, Muhammad Attique Khan, Robertas Damaševičius · International Journal of Web and Grid Services · 2024
The emergence of fifth-generation (5G) mobile communication technologies has propelled the advancement of the internet of things (IoT). Nevertheless, the intricate nature of the IoT mobile communication environment and the fluctuating characteristics of the signal's present substantial obstacles to current spectrum detection techniques for future communication. Hence, an artificial intelligent spectrum sensing technique is introduced, which integrates artificial intelligent, IoT and denoising autoencoder (DAE) with an enhanced long-short-term memory (LSTM) neural network. The DAE utilises encoding and decoding to retrieve the fundamental structural characteristics of mobile signals, while the enhanced LSTM spectrum sensing classifier model incorporates previous moment information features to classify the time-series signal sequences. This method has demonstrated a 45% improvement in perception performance compared to SVM, RNN, LeNet5, LVQ, and Elman algorithm.