Downlink scheduling with guarantees on the probability of short-term throughput

We consider the problem of scheduling multiple transmissions on the downlink of a wireless network with performance guarantees in the form of the probabilities that short term throughputs exceed user specified thresholds. Many interactive data applications have some degree of a latency requirement, and measure performance by throughput over a relatively short time interval. We refer to the fraction of time such user throughput reaches a predefined rate threshold or higher as tail probability. The problem is formulated as maximizing the minimum ratio of tail probability to the user specified probability threshold. We present necessary and sufficient optimality conditions for the case in which the time interval of interest is consistent with the time scale of channel variation. An online algorithm is proposed which can achieve the optimality. For the case in which the time interval of interest is large compared to the time scale of channel variation, we develop an online algorithm which attempts to maximize the minimum normalized tail probability by taking the advantage of channel variation over users and over time. Simulation results demonstrate that the proposed algorithm can achieve better performance than other algorithms such as the proportional fair algorithm and the Max C/I algorithm.