Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs With Graph Convolutional Networks

DOI DOI DOI 機関リポジトリ (HANDLE) HANDLE ほか1件をすべて表示 一部だけ表示 被引用文献2件 参考文献37件 オープンアクセス

説明

For densely deployed wireless local area networks (WLANs), this paper proposes a deep reinforcement learning-based channel allocation scheme that enables the efficient use of experience. The central idea is that an objective function is modeled relative to communication quality as a parametric function of a pair of observed topologies and channels. This is because communication quality in WLANs is significantly influenced by the carrier sensing relationship between access points. The features of the proposed scheme can be summarized by two points. First, we adopt graph convolutional layers in the model to extract the features of the channel vectors with topology information, which is the adjacency matrix of the graph dependent on the carrier sensing relationships. Second, we filter experiences to reduce the duplication of data for learning, which can often adversely influence the generalization performance. Because fixed experiences tend to be repeatedly observed in WLAN channel allocation problems, the duplication of experiences must be avoided. The simulation results demonstrate that the proposed method enables the allocation of channels in densely deployed WLANs such that the system throughput increases. Moreover, improved channel allocation, compared to other existing methods, is achieved in terms of the system throughput. Furthermore, compared to the immediate reward maximization method, the proposed method successfully achieves greater reward channel allocation or realizes the optimal channel allocation while reducing the number of changes.

収録刊行物

  • IEEE Access

    IEEE Access 8 31823-31834, 2020-02-11

    Institute of Electrical and Electronics Engineers (IEEE)

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