Reliable port communications support maritime applications such as vessel navigation and cargo tracking for a large number of mobile users on ships, but the quality of services (QoS) such as data rate and energy consumption is severely degraded by inter-cell interference. In this paper, we propose a deep reinforcement learning (RL)-based interference coordination scheme for port communications to reduce the transmission latency and energy consumption, and improve the data rate. Based on the signal-to-interference plus noise ratio, the channel gains, the estimated interference levels and the transmission latency, the base station chooses the transmit power and downlink bandwidth constraint to avoid choosing risk policies that cause the communication performance degradation. In addition, a two-level hierarchical structure with two convolution networks and four fully connected layers is designed to reduce the algorithm complexity and enhance the convergence speed. Simulation results verify the performance gain of the proposed scheme in terms of the data rate, the transmission latency, and the energy consumption compared with the benchmark.


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    Title :

    Reinforcement Learning Based Interference Coordination for Port Communications


    Contributors:
    Li, Siyao (author) / Liu, Chuhuan (author) / Chen, Yifan (author) / Xiao, Liang (author) / Yang, Helin (author)


    Publication date :

    2024-06-24


    Size :

    2795037 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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