摘要
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.