Exploiting mobility in proportional fair cellular scheduling: Measurements and algorithms

Proportional Fair (PF) scheduling algorithms are the de-facto standard in cellular networks. They exploit the users´ channel state diversity (induced by fast-fading), and are optimal for stationary channel state distributions and an infinite time-horizon. However, mobile users experience a non-stationary channel, due to slow-fading (on the order of seconds), and are associated with basestations 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 rely on the prediction mechanism. Our trace-based simulations consider instances of the PF²S 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.