Adaptive proportional fair scheduling with global-fairness

In practical communication systems, there are always multiple subscribers competing for limited resources, such as time and frequency, hence effective user scheduling is essential to multi-user communications in achieving good system throughput and fairness performance. The conventional proportional fair (PF) scheduling achieves fairness at the cost of system spectral efficiency (SE) loss. Such fairness is of long-term feature, i.e., all the users’ scheduling probabilities become approximately the same only when the observation time is long enough. Therefore, PF cannot guarantee the fairness for subscribers who enter the system temporarily or stay in the system for a short period of time. In addition, delay requirement of real-time-service users can hardly be met with conventional PF. In order to remedy these deficiencies, we propose adaptive proportional fair (APF) scheduling algorithms. In each time slot, the infrastructure node, e.g., base station, dynamically adjusts the forgetting factor based on the variance of all the subscribers’ scheduling priorities, so that users’ scheduling weights can be adaptively updated. Our in-depth simulation results show that compared to conventional PF, APF can not only achieve both long-term and short-term fairness which we refer to global-fairness, but also obtain high system SE. Moreover, users’ delay performance can be obviously improved.