Dynamic Spectrum Access for Femtocell Networks: A Graph Neural Network Based Learning Approach
Dynamic Spectrum Access for Femtocell Networks: A Graph Neural Network Based Learning Approach
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DOI：
10.1109/icnc47757.2020.9049731
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
He Jiang;Haibo He;Lingjia Liu
中科院分区：
文献类型：
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作者：
He Jiang;Haibo He;Lingjia Liu
研究了在不同业务负载下，毫微微蜂窝网络的动态频谱接入问题。我们用冲突图来描述毫微微蜂窝网络的干扰关系，并采用图形博弈作为信道接入协调机制。提出了一种基于图神经网络的体系结构，它直接将业务负载映射到每个毫微微小区的信道接入方案。在我们的方法中，每个毫微微蜂窝基站首先根据来自其邻居的信息估计所有可用信道的质量，然后访问质量最高的信道。一个多智能体强化学习框架的目的是训练所提出的架构，使信道质量的准确估计。
This paper concerns the dynamic spectrum access problem for femtocell networks, where traffic loads are different among cells. We model the interference relationship of the femtocell networks with conflict graphs, and a graphical game is employed as the channel access coordination mechanism. A graph neural network based architecture is proposed, which directly maps traffic loads to the channel access scheme for each femtocell. With our method, each femtocell first estimates the qualities of all available channels based on the information from its neighbors, and then the channels of the highest quality are accessed. A multiagent reinforcement learning framework is designed to train the proposed architecture to make accurate estimations of channel quality.