Order statistics based CDF scheduling methods in multiuser heterogeneous systems

In modern heterogeneous wireless networks, the task of supporting fairness along with user priorities and concurrently achieving the highest possible system throughput is desirable and challenging. Herein, a class of practical cumulative distribution function (CDF) scheduling algorithms are developed to achieve these goals. These algorithms are used when the channel fading model is unknown. The mapping from channel quality information (CQI) to the real CDF is unknown but is constructed exploiting the order statistics of the CQI sequence. The constructed CDF mapping methods are shown to converge to the actual CDF. Specifically, one algorithm uses the expected value of the ordered CDF scheduling while others called Non-parametric CDF scheduling (NPCS) algorithms reconstructs the CDF with an extra interpolation step. By collecting a moderate number of CQI data, the algorithms almost achieve the system throughput of CDF scheduling as if the CDF is known. Throughout the work, CDF scheduling algorithms, supported by simulations, are shown to be able to effectively support fairness and frequently outperform, and are potential alternatives to, the well known Proportional Fair (PF) scheduling method.