Differentiated Service Based on Reinforcement Learning in Wireless Networks
In this paper, we propose a global quality of service management applied to DiffServ environments and IEEE 802.11e wireless networks. Especially, we evaluate how the IEEE 802.11e standard for Quality of Service in Wireless Local Area networks (WLANs) can interoperate with the Differentiated Services (DiffServ) architecture for end-to-end IP QoS. An Architecture for the integration of traffic conditioner is then proposed to manage the resources availability and regulate traffic in congestion situation. This traffic conditioner is modelled as an agent based on reinforcement learning.
KeywordsWireless networks IEEE 802.11e DiffServ end-to-end QoS Traffic conditioner Reinforcement learning
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