Machine Learning-based Spectrum Allocation using Cognitive Radio Networks

A scarcity of frequencies arises from the increased demand for the Industrial Internet of Things (IIoT) and networked systems in warehouse operations. This puts an additional burden on available bandwidth in cellular networks. This problem can be tackled by cognitive radio networks (CRNs), which use spectrum sensing to track and access unused frequencies, increasing spectrum efficiency. However CRNs have been extensively studied in several IoT projects, this study is the initial to examine the utilisation of CRN to intelligently manage the use of available radio frequencies. Two macro base stations are the central hubs of a network, communicating with IIoT devices in warehouse settings. This paper investigates the utilisation of CRN with machine-learning algorithms to intelligently manage the spectrum for connected IoT devices for intelligent Warehouse settings. A range of machine learning methods, including Support Vector Machine (SVM), k-nearest Neighbors (KNN), Decision Tree, Random Forest, and Naive Bayes, are provided to identify accessible bands for the best possible spectrum allocation and to recognize key users. Based on criteria like accuracy, precision, recall, and score for all ML techniques, the system’s performance is assessed. The numerical results demonstrate a noteworthy 20% reduction in false positives and a substantial improvement in cooperative spectrum sensing accuracy, which in turn improves the effectiveness of IIoT operations in warehouse environments.