Deep learning‐driven distributed communication systems for cluster online educational platform considering human–computer interaction. (26th October 2021) - Zhou, Jinrong
Summary: The wireless network environment has become increasingly complex, diverse, and dynamic. In recent years, the introduction of new concepts such as green networks and smart networks has diversified the optimization objectives of the spectrum resource management. Under this background, this paper proposes the novel deep learning‐driven distributed communication systems for cluster online educational platform considering human–computer interaction. Before the modeling, the literature review and the state‐of‐the‐art methodologies are introduced. For the framework design, first, the novel distributed communication system is designed considering the integration of the Q‐learning under the deep neural network structure. Then, the cluster computing model is integrated for the system to enhance the robustness. Later, the human–computer interaction together with the movement tracking models is proposed to finalize the interactive online platform. The simulation results show that the proposed algorithm can extract the information accurately, and transmission efficiency is guaranteed. Compared with the latest platforms, the proposed outperforms from the aspects of accuracy and efficiency. Abstract : This paper proposes the novel deep learning‐driven distributed communication systems for cluster online educational platform considering human–computer interaction. First, the novel distributed communication system is designed considering the integration of the Q‐learning under the deep neural network structure. Then, the cluster computing model is integrated for the system to enhance the robustness. Later, the human–computer interaction together with the movement tracking models is proposed to finalize the interactive online platform.