A Reinforcement Learning Approach to Self-Configuring Edge Wireless Networks
Date
2019-01-15
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Abstract
Wireless Internet access has brought legions of heterogeneous applications all
sharing the same resources. However, current wireless edge networks that cater to
worst or average case performance lack the agility to best serve these diverse sessions.
Simultaneously, software reconfigurable infrastructure has become increasingly mainstream to the point that dynamic per packet and per flow decisions are possible at
multiple layers of the communications stack. Exploiting such reconfigurability requires the design of a system that can enable a configuration, measure the network
performance statistics (Quality of Service), learn the impact on the application performance (Quality of Experience), and adaptively select a new configuration. The
goal of this work is to design, develop and demonstrate a reinforcement learning
approach to self-configuring wireless edge networks that in instantiates this feedback
loop. Our context is that of reconfigurable queueing, and we use the popular application of video streaming as our example. Through simulation and experimental
validation, we show how measurement, learning and control are combined to enable
high QoE video streaming on our platform.
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Keywords
wireless networks, reinforcement learning, self-configuring networks, Q-learning