A Generalized Gradient Scheduling Algorithm in Wireless Networks for Variable Rate Transmission

Abstract

Abstract-Average transmission rate and rate oscillation are two important performance metrics for most wireless services. Both are often needed to be optimized in multi-user scheduling and resource management. In this paper we introduce a utility function that increases with average rate but decreases with rate variance. It is capable of facilitating resource allocation with flexible combinations of the two performance metrics. A generalized gradient scheduling algorithm (GGSA) is then developed to maximize the proposed utility. It is shown that the best scheduler should maximize the sum of concave functions of instantaneous transmission rates in order to maximize the utility of average rate and rate oscillation. The scheduler reduces to the traditional gradient scheduling algorithm when the rate variance term in the new utility function is omitted. We analyze the dynamics of average transmission rates and rate variances using ordinary differential equation and show that GGSA is asymptotically optimal under the condition that the transmission rate vector, after an appropriate scaling, converges to a fixed vector as time goes into infinity.

Multiple Utility Factors-Based Parallel Packet Scheduling for Broadcast/Unicast in BWM Systems

“…Furthermore, to satisfy multimedia services with diverse QoS, MPF scheduling with QoS-aware priority factor [7], QoS-oriented cross-layer packet scheduling for real-time services [8] and the adaptive scheduling with channel quality [9] are proposed in early work. In addition, to consider more utility factors to improve scheduling performance with QoS guaranteed, the utility-based resource allocation and scheduling [10], the generalized gradient scheduling by maximizing utility function of the instantaneous transmission rate [11] and the utility based scheduling with the average rate, packet delay and average waiting time [12] are studied. However, the utility-based scheduling with multiple QoS factors leads to high complexity.…”