Efficient resource allocation scheme with grey relational analysis for the uplink scheduling of 3GPP LTE networks

Ruey-Rong Su, I. Hwang
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引用次数: 8

Abstract

Appropriate bandwidth allocation is an important issue to benefit the long Term Evolution (LTE) networks performance. Single-carrier FDMA (SC-FDMA) multiple access scheme has been chosen for the 3GPP LTE uplink scheduling. However, it requires that all the sub carriers allocated to a single user must be in contiguous frequency band within each time slot. This constraint limits the scheduling flexibility. The resource allocation algorithm in frequency domain must take this constrain into consideration to maximize its scheduling objectives. An Efficient Resource Allocation algorithm with Grey Relational Analysis (ERAGRA) is proposed and compared with the others algorithms based on the throughput and fairness in this research. The ERAGRA algorithm is a channel-aware traffic resource allocation algorithm which aims at enabling uplink traffic delivery on ideal and non-ideal channels. This algorithm is evaluated using 3GPP LTE simulation model. A key performance index (KPI) is defined as the combination of total throughput and fairness. ERAGRA and two other scheduling algorithms: Best CQI and Round Robin has been evaluated through simulation based on this KPI. Simulation results indicate that the proposed ERAGRA algorithm effectively improves the average normalized system throughput of Best CQI by 6.22 % and fairness by 2.74 %, while improves the average normalized system throughput of Round Robin by 38.67 % and fairness by 10.26 %. The proposed scheduling scheme achieves a near optimal solution for maximizing system throughput and fairness of resource utilization.
基于灰色关联分析的3GPP LTE网络上行调度高效资源分配方案
合理的带宽分配是提高LTE网络性能的一个重要问题。3GPP LTE上行调度选择了单载波FDMA (SC-FDMA)多址方案。但是,它要求分配给单个用户的所有子载波必须在每个时隙内处于连续频带。这个约束限制了调度的灵活性。频域资源分配算法必须考虑这一约束,才能使调度目标最大化。提出了一种基于灰色关联分析的高效资源分配算法(ERAGRA),并在吞吐量和公平性的基础上与其他算法进行了比较。ERAGRA算法是一种信道感知的业务资源分配算法,旨在实现上行业务在理想信道和非理想信道上的传输。利用3GPP LTE仿真模型对该算法进行了评估。关键性能指数(KPI)定义为总吞吐量和公平性的组合。在此KPI的基础上,通过仿真对ERAGRA和其他两种调度算法:Best CQI和Round Robin进行了评估。仿真结果表明,提出的ERAGRA算法有效地将Best CQI的平均归一化系统吞吐量提高了6.22%,公平性提高了2.74%,将Round Robin的平均归一化系统吞吐量提高了38.67%,公平性提高了10.26%。该调度方案实现了系统吞吐量最大化和资源利用公平性的近似最优解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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