To address the significant issue of hidden terminal interference that severely impacted resource management in ultra-dense Internet of things(UD-IoT)environments,a deep deterministic gradient-based conflict-free resource alloca-tion strategy using graph convolution neural network was proposed.The conflict graph model was constructed by em-ploying matrix transformations to represent potential hidden terminal interference among devices.Then,using the con-cepts of maximal cliques and hypergraph theory,the conflict graph model was transformed into a conflict hypergraph model.This transformation allowed the conflict-free resource allocation problem to be formulated as a hypergraph vertex coloring problem.A deep deterministic gradient-based conflict-free resource allocation algorithm,leveraging graph con-volutional neural network reinforcement learning,was developed to achieve conflict-free resource allocation and maxi-mize resource reuse.Simulation results demonstrated that the proposed algorithm achieved higher resource reuse rates and throughput compared to existing methods,providing superior performance in ultra-dense IoT.
关键词
超密集物联网/资源分配/深度强化学习/图卷积神经网络
Key words
ultra-dense Internet of things/resource allocation/deep reinforcement learning/graph convolutional neural network
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