Optimal Spectrum Sensing Framework for Cognitive Radio Networks Using Attention‐Based Autoencoder With Multi‐Scale Capsule Network

ABSTRACT Wireless communication industry's explosive growth over the last 10 years caused a shortage of resources since its demand has greatly increased. A technology called cognitive radio (CR) was created to make efficient utilization of the spectrum from radio sources. The effectiveness of CR is significantly influenced by the spectrum sensing (SS) function, and it is the primary function of CR, which helps to discover available spectrum for better spectrum utilization and reduce detrimental conflict with approved users. In addition, the conventional models cause high computational complexity in the SS. In this work, an adaptive SS system for CR networks is developed to identify unused bands of frequencies in order to get outside of these constraints. Initially, the essential synthetic data are gathered manually, and these data are used by the suggested adaptive and residual hybrid network (A‐RHN) for SS. The A‐RHN network is developed by using an attention‐based Autoencoder with a multi‐scale capsule network (AA‐MCN). Moreover, the effectiveness of this model is enhanced by optimizing parameters via the revised uniform variable‐based addax optimization algorithm (RUV‐AOA). The proposed model enables more efficient use of the available spectrum by avoiding transmitting on frequencies that are already in use.