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Communication Dans Un Congrès Année : 2019

Drone-assisted cellular networks: a multi-agent reinforcement learning approach

Résumé

Drone-cell technology is emerging as a solution to support and backup the cellular network architecture. cell-drones are flexible and provide a more dynamic solution for resource allocation in both scales: spatial and geographic. They allow to increase the bandwidth availability anytime and everywhere according the continuous rate demands. Their fast deployment provide network operators with a reliable solution to face sudden network overload or peak data demands during mass events, without interrupting services and guaranteeing better QoS for users. With these advantages, drone-cell network management is still a complex task. We propose in this paper, a multi-agent reinforcement learning approach for dynamic drones-cells management. Our approach is based on an enhanced joint action selection. Results show that our model speed up network learning and provide better network performance.
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Dates et versions

hal-02101394 , version 1 (10-05-2019)

Identifiants

Citer

Seif Eddine Hammami, Hossam Afifi, Hassine Moungla, Ahmed E. Kamel. Drone-assisted cellular networks: a multi-agent reinforcement learning approach. ICC 2019: 53rd International Conference on Communications, May 2019, Shanghai, China. pp.1-6, ⟨10.1109/ICC.2019.8762079⟩. ⟨hal-02101394⟩
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