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“…Therefore, RL approaches need to evaluate the impact of the chosen configuration. For instance, in [ 15 ], based on a simple interference model, the authors proposed a Deep Q-Network (DQN) that learns how many channels need to be bonded to satisfy future demands. Here, the authors assumed that different WLANs are connected to a central controller, allowing information exchange.…”
Section: Related Workmentioning
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“…Therefore, RL approaches need to evaluate the impact of the chosen configuration. For instance, in [ 15 ], based on a simple interference model, the authors proposed a Deep Q-Network (DQN) that learns how many channels need to be bonded to satisfy future demands. Here, the authors assumed that different WLANs are connected to a central controller, allowing information exchange.…”
Section: Related Workmentioning
“…We show these scenarios in Section 6 . Unlike [ 15 ], our approach does not define the best CB policy that a WLAN needs to apply to maximize its reward. Our goal is that the ML model learns how the wireless interactions affect the throughput of a given WLAN without any explicit assumptions, such as user demand or user mobility patterns.…”
Section: Related Workmentioning
“…are also a parameter to be taken into account, as well as other performance metrics such as delay and throughput, DRL techniques are considered [135], [136]. Lastly, SL techniques are also used to predict future states [137], [139] and so be able to react in advance in case when predicted values are below the expectations.…”
Section: Channel Bondingmentioning
“…DRL is considered in [135], [136]. In [135], the channel allocation problem (i.e., group of selected channels and position of the primary channel) in a scenario with multiple BSSs is addressed.…”
Section: Channel Bondingmentioning
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