Exploiting Mobility in Proportional Fair Cellular Scheduling: Measurements and Algorithms
Abstract: Proportional Fair (PF) scheduling algorithms are the de facto standard in cellular networks. They exploit the users' channel state diversity (induced by fastfading) and are optimal for stationary channel state distributions and an infinite time-horizon. However, mobile users experience a nonstationary channel, due to slow-fading (on the order of seconds), and are associated with base stations for short periods. Hence, we develop the Predictive Finite-horizon PF Scheduling ((PF)2S) Framework that exploits mobility. We present extensive channel measurement results from a 3G network and characterize mobility-induced channel state trends. We show that a user's channel state is highly reproducible and leverage that to develop a data rate prediction mechanism. We then present a few channel allocation estimation algorithms that exploit the prediction mechanism. Our trace-based simulations consider instances of the (PF)2S Framework composed of combinations of prediction and channel allocation estimation algorithms. They indicate that the framework can increase the throughput by 15%-55% compared to traditional PF schedulers, while improving fairness.