A Spectral‐Spatial Dual Encoder With Bio‐Inspired Swarm Adaptation for Cognitive Spectrum Sensing Using Real‐World RF Signal Data

ABSTRACT Cognitive Spectrum Sensing (CSS) stands as a foundational component in 5G and emerging 6G wireless communication systems, enabling intelligent identification and dynamic utilisation of underutilised spectrum bands. However, the implementation of CSS in dense and heterogeneous 5G/6G environments presents significant challenges, including high spectral dynamics, multi‐protocol interference, and the requirement for real‐time decision‐making across diverse frequency bands. Existing methods such as deep belief networks, CNN‐PSO hybrids, and DQN‐based models suffer from limited adaptability, insufficient spatial‐temporal learning, and poor generalisation in real‐world RF environments. The proposed model includes a dual‐stream deep learning architecture which has a 1D convolutional neural network (CNN)‐based spectral encoder and a graph convolutional network (GCN)‐based spatial encoder for extracting the frequency‐domain and node‐topology features. Experimental analysis of proposed model is performed using the Real‐World Wireless Communication Dataset containing Wi‐Fi, LTE, and 5G RF signals. The dataset is pre‐processed using Fast Fourier Transformation (FFT) transformation and labelled through a signal‐power‐based thresholding mechanism. Results indicate the Spectral‐Spatial Dual Encoder with Bio‐Inspired Swarm Adaptation (SSDE‐BSA) achieves an accuracy of 96.1%, an F1‐score of 96.1%, and a spectral efficiency of 91. These results confirm the model's superiority in adapting to real‐world spectrum dynamics, offering a robust and scalable solution for cognitive spectrum sensing in next‐generation wireless networks.