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Automatic MAC protocol selection in wireless networks based on reinforcement learning
انتخاب خودکار پروتکل MAC در شبکه های بی سیم بر اساس یادگیری تقویت-2020 Existing MAC protocols do not address the dynamism and complexity of the environment and applications
of today’s wireless networks. The running applications and the environment change all the time, and as a
consequence the requirements of the wireless transmissions change. For example, in one moment a client is
transmitting video, requiring high throughput; next it will control a robotic arm, requiring bounded delays.
In this example, a contention-based MAC protocol would cope with flexible traffic demands, however it does
not meet the delay constraints. Reservation based protocols, meanwhile, provide performance guarantees, but
at a higher overhead. Hence, wireless networks require adaptive techniques that change how the network
reacts over time. To that end, we propose SOMAC (Self-Organizing MAC), a system that uses reinforcement
learning techniques to switch the MAC protocol in structured wireless networks according to the ongoing
network demand. The novelty of SOMAC lies in its use of reinforcement learning, which solves the following
shortcomings in the literature: (i) the lack of models that cope with changes in its environment or lack of
representative data during training; (ii) the capacity to self-optimize based on a number of metrics. To showcase
its genericity, we evaluated the model using two different optimization metrics (throughput and delay) on a
testbed.
Results indicate that our solution performs similar to an oracle choosing the most suitable MAC protocol
from the list of implemented protocols up to 90% of the time. Further, SOMAC outperforms the state of the
art by up to 20% in terms of protocol selection. Keywords: MAC sub-layer | MAC protocol selection | Switching MAC protocols | Reinforcement learning |
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