This work proposes a novel mechanism for management, orchestration and flow control in the context of the device-to-device (D2D) to deal with load balancing using the deep Q-learning (DQN) technique. To do so, we implemented a D2D network simulation environment, using the ParticiptAct dataset to evaluate the load of the cell towers in a region of Italy. The Gauss-Markov and Gilbert-Elliott models were used for mobility and packet loss, respectively, where it was considered that the towers had a disconnected coverage area, hence forming a Voronoi space. We used a Gaussian process to predict the load of the towers when they receive the packet, and a DQN to perform the balance of load of the network. This proposal presents better results than the baseline, concerning the metrics used, as well as presenting some perspectives for a future unfolding of this work.
Barros P.H., Cardoso-Pereira I., Foschini L., Corradi A., Ramos H.S. (2019). Load balancing in D2D networks Using Reinforcement Learning. Institute of Electrical and Electronics Engineers Inc. [10.1109/ISCC47284.2019.8969767].
Load balancing in D2D networks Using Reinforcement Learning
Foschini L.;Corradi A.;
2019
Abstract
This work proposes a novel mechanism for management, orchestration and flow control in the context of the device-to-device (D2D) to deal with load balancing using the deep Q-learning (DQN) technique. To do so, we implemented a D2D network simulation environment, using the ParticiptAct dataset to evaluate the load of the cell towers in a region of Italy. The Gauss-Markov and Gilbert-Elliott models were used for mobility and packet loss, respectively, where it was considered that the towers had a disconnected coverage area, hence forming a Voronoi space. We used a Gaussian process to predict the load of the towers when they receive the packet, and a DQN to perform the balance of load of the network. This proposal presents better results than the baseline, concerning the metrics used, as well as presenting some perspectives for a future unfolding of this work.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.