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- W4313209850 abstract "Due to the popularity of mobile devices, more WIFI access points (APs) are being installed in public spaces, including train stations, airports, campuses, and shopping centers. Any station in WiFi networks will often connect to the AP that is closest to it based on the RSSI value that is the strongest. This user association approach will result in unfair bandwidth distribution and load imbalance when WiFi networks have many stations and an uneven distribution of those stations. By separating the control plane and data plane, software-defined networks (SDN) enable WiFi networks that are programmable, controllable, measurable, and manageable. This paper represents a novel load balancing mechanism based on reinforcement learning and SDN. In our proposed scheme, the center controller collects the number of associated stations with APs and traffic information on each AP’s backhaul link. Then the controller decides the optimal station migration scheme to achieve load balancing among APs, based on collected information and reinforcement learning algorithm. The simulation results illustrate that our algorithm outperforms the RSSI method in terms of throughput of stations and average station number of each AP." @default.
- W4313209850 created "2023-01-06" @default.
- W4313209850 creator A5027835055 @default.
- W4313209850 date "2022-12-13" @default.
- W4313209850 modified "2023-10-01" @default.
- W4313209850 title "RL-Based Load Balancing for Software-Defined WiFi Networks" @default.
- W4313209850 doi "https://doi.org/10.3233/faia220565" @default.
- W4313209850 hasPublicationYear "2022" @default.
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