Framework for analysis of opportunistic schedulers: average sum rate vs. average fairness

Channel aware and opportunistic scheduling algorithms exploit the channel knowledge and fading to increase the average throughput. The maximum throughput scheduler (MTS) transmits only to the best user at a time. It is unfair to users at the cell edge. Obviously, there is a tradeoff between average throughput and fairness in the system. In this work, we study four representative schedulers, namely the MTS, the proportional fair scheduler (PFS), the (relative) opportunistic round robin scheduler (ORS), and the round robin scheduler (RRS). We show that the average sum rate performance and the average worst-case delay depend strongly on the user distribution within the cell. MTS gains from asymmetrically distributed users whereas the other three schedulers suffer. On the other hand, the average fairness of MTS and PFS decreases with asymmetrical user distribution. The key contribution of this paper is to put these tradeoffs and observations on a solid theoretical basis. The scaling laws of the average sum rate with the number of users as well as of the average worst-case delay are derived. Both the PFS and the ORS provide a reasonable performance in terms of throughput and fairness. However, PFS outperforms ORS for symmetrical user distributions, whereas ORS outperforms PFS for asymmetrical user distributions.