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- W2899863586 abstract "Due to the rapid development in mobile devices such as smart phone and iPad, video traffic has almost become the main traffic part in current wireless networks. However, limited bandwidth, unstable channel conditions and higher demand for Quality of Service (QoS) make it challenging to transmit high quality video streaming in the multi-user communication scenario. In order to improve the efficiency of radio resources, it is necessary to exploit intelligent scheduling rules in wireless communication technologies such as Long Term Evolution (LTE). In this paper, we propose a novel downlink scheduler that dynamically adopts different scheduling strategies depending on the channel quality and traffic delay at each Transmission Time Interval (TTI). Q-Learning is utilized to learn the best matching scheduling decisions according to the previous experience. The simulation results show that the proposed algorithm has better performance especially for video traffics." @default.
- W2899863586 created "2018-11-16" @default.
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- W2899863586 date "2018-08-01" @default.
- W2899863586 modified "2023-09-24" @default.
- W2899863586 title "A Novel Downlink Scheduler Based on Q-Learning for Video Traffic in LTE Networks" @default.
- W2899863586 doi "https://doi.org/10.1109/icnidc.2018.8525617" @default.
- W2899863586 hasPublicationYear "2018" @default.
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