Multi-Targeted Downlink Scheduling for Overload-States in LTE Networks: Proportional Fractional Knapsack Algorithm With Gaussian Weights

Abstract

In Long-Term Evolution (LTE) and beyond systems, radio resource scheduling mechanism plays one of the main roles in system performance maximization. From this perspective, due to the conflicting quality requirements of different traffic types, providing a compromise among all performance targets for heterogeneous traffic is difficult. Moreover, the centralized scheduling mechanism for the ever growing number of users along with the massive variety of services, especially in overload states, is infeasible due to the extensive cost of information acquisition and computations. In this paper, we design resource scheduling policies for supporting the efficient delivery of heterogeneous traffic in overload states of a cell. To this end, we cast the class-based bearer-level resource distribution problem as a Proportional Fractional Knapsack model. The objective of the formulated problem is to meet Quality of Service (QoS) requirements and provide fairness for all standardized service classes. Since the solution of this problem is computationally expensive, due to the uncertainty and limited information on network and user operation, we develop a Gaussian-based analytical model and drive a formula for simplified computation of the weight of service bearers. Then, we propose Proportional Fractional Knapsack algorithm for guaranteeing effective utilization of resources for heterogeneous traffic. Finally, performance evaluation results are provided and demonstrate that the proposed scheduling approach can provide a significant level of fairness, in balance with the QoS and throughput performance targets, comparable with optimal ones.

Keywords

• Fairness
• Gaussian weight
• heterogeneous traffic
• Proportional Fractional Knapsack
• Quality of Service
• resource scheduling