Load balancing in D2D networks Using Reinforcement Learning
| Autor: | Pedro H. Barros, Antonio Corradi, Isadora Cardoso-Pereira, Luca Foschini, Heitor S. Ramos |
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| Přispěvatelé: | Barros P.H., Cardoso-Pereira I., Foschini L., Corradi A., Ramos H.S. |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Flow control (data)
Gaussian proce Computer science Network packet Distributed computing 020206 networking & telecommunications 02 engineering and technology Load balancing (computing) Network simulation Packet loss 020204 information systems Reinforcement learning 0202 electrical engineering electronic engineering information engineering Predict load D2D network Load balancing |
| Zdroj: | ISCC |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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