SOM-Based Leader Selection Strategies for Cooperative Spectrum Sensing in Multi-Band Multi-User 6G CR IoT

In 6G Cognitive Radio Internet of Things (CR-IoT) networks, multi-band spectrum sensing cooperatively provides access to extensive spectrum resources. The suggested learning-based multi-band multi-user cooperative spectrum sensing (M2CSS) scheme addresses intelligent spectrum access challenges. A cooperative strategy is introduced into a dueling deep Q network to facilitate multi-user reinforcement learning. This study selects the most suitable IoT secondary users (SU) to sense channels using the proposed learning-based M2CSS scheme. With the restriction that each IoT SU can serve as a front-runner for a single network and that there will only be one leader for individual frequency, the proposed work expresses an optimization difficulty in choosing leaders through k-means and SOM, who can efficiently interact with other SUs. Next, choose matching cooperative SUs for each frequency and express additional optimization problems. Following this phase, a subset of cooperative secondary users (SUs) senses frequencies and employs accurate knowledge to determine the channels' availability in a distributed manner. The simulation findings demonstrate significant improvements in detection performance, preventing the misuse of specific devices, providing reliable sensing data over extensive IoT connections, and achieving energy efficiency—all essential for IoT implementations. These advantages make the proposed M2CSS system suitable for the massive machine-type communications anticipated in 6G IoT scenarios.