This paper aims to address the issue of low through-put for users, caused by the random movement of High-Altitude Platform Stations (HAPS) due to winds. The proposed solution involves developing an Equal Clustering (EC) approach that groups users into high-density clusters, ensuring an equal num-ber of users in each cluster while maintaining low complexity. To further enhance the system's throughput performance, we fine-tune the antenna parameters using a Deep Q-Network (DQN) and the results of the EC clustering. To evaluate the effectiveness of the proposed method, we compare it with three Reinforcement Learning (RL)-based approaches and a K-Means clustering-based method. Simulation results indicate that both the EC method and the EC-aided DQN method successfully enhance the Cumulative Distribution Function (CDF) performance of throughput distribution when compared to the RL-based method for both rotation and shift scenarios. Furthermore, the EC-aided DQN method outperforms the K-Means clustering-based method in terms of the CDF of throughput performance.


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    Titel :

    A Low-Complexity Clustering-Aided DQN Method for Dynamic Antenna Control in HAPS


    Beteiligte:
    Bouazizi, Mondher (Autor:in) / Yang, Siyuan (Autor:in) / Ohtsuki, Tomoaki (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    3417150 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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