Decentralized Meta-Reinforcement Learning with Graphical Neural Networks for Dynamic Spectrum Access in 5G IoT Environments

5G IoT networks which use cognitive radio technology need dynamic spectrum access (DSA) to achieve fast response times during extreme environmental changes while managing extensive network operations. The research introduces a decentralized graph-based meta-reinforcement learning framework which enables cognitive IoT devices to learn spectrum access methods through decentralized learning. The proposed method uses Model-Agnostic Meta-Learning (MAML) with Graph Neural Networks (GNNs) to enable structure-aware few-shot adaptation which requires only local observations and neighbor interactions to function. Agents require between 1 and 5 gradient steps to develop new spectrum adaptation capabilities. The proposed framework achieved 83% spectrum utilization, an average throughput of 1.6 packets/slot, Jain's fairness index of 0.92, and a collision rate of 5%, outperforming Meta-RL and GCN-RL baselines. The study demonstrates that decentralized Meta-RL with relational learning offers an effective and scalable method for managing intelligent spectrum in future wireless IoT networks.