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Abstract

Existing channel-aware scheduling work has mainly focused on scheduling in small timescales, that is, tens to hundreds of seconds. We propose to use long-term user profiles to provide useful statistical information on future network conditions in large timescales. We design scheduling algorithms based on Markov decision theory. We collect and use a large set of real-life traces from the general public. Extensive trace-driven evaluations show that many real mobile users can benefit from our framework. In addition, we compare our framework against state-of-the-art algorithms and observe significant performance differences because the existing algorithms were not designed for the large timescale scenario.