Dynamic spectrum access for Internet-of-Things with joint GNN and DQN

With the rapid growth in access demand for Internet of Things (IoT) devices, effective utilization of spectrum resource has become a key challenge to ensure reliable communications. Traditional dynamic spectrum access methods are inefficient when there are too many device accesses, channel reductions, and channel quality deterioration. In this paper, we propose a dynamic spectrum access method based on a fusion algorithm of graph neural network (GNN) and deep Q network (DQN), improving spectrum access efficiency while maintaining a good access success accuracy. Compared with traditional DQN, the computation time can be reduced by over 35%. Our approach first uses GNN to interact with the environment and predict the state of the IoT spectrum environment. Subsequently, automatic learning and optimization of spectrum access policies are achieved by selecting the mobile IoT user's actions based on these predicted states using the DQN's target network, experience playback, and reinforcement learning techniques. Simulation results show that the system model based on the proposed method can operate with better efficiency than the conventional method while maintaining a good channel access rate and channel quality.