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QoS保障的LTE-A飞蜂窝资源块分配与MCS选择研究
引用本文:李龙飞,陈 昕,向旭东. QoS保障的LTE-A飞蜂窝资源块分配与MCS选择研究[J]. 计算机科学, 2015, 42(8): 95-100, 131
作者姓名:李龙飞  陈 昕  向旭东
作者单位:北京信息科技大学计算机学院网络文化与数字传播北京市重点实验室 北京100101,北京信息科技大学计算机学院网络文化与数字传播北京市重点实验室 北京100101,北京科技大学计算机与通信工程学院 北京100083
基金项目:本文受国家自然科学基金面上项目(61370065)资助
摘    要:针对LTE-A飞蜂窝网络下行链路的资源块(Resource Block,RB)分配与调制编码策略(Modulation-and-Co-ding Scheme,MCS)选择问题,构建了整数线性规划模型,以在保障每个飞蜂窝用户最小吞吐量的需求下,最大化飞蜂窝系统吞吐量。其中,吞吐量是衡量网络性能最重要的服务质量(Quality of Service,QoS)指标之一。鉴于此问题是一个NP难问题,提出了一种ACOGA智能优化算法。该算法结合遗传算法(Genetic Algorithm,GA)与蚁群优化(Ant Colony Optimization,ACO)算法,可实现RB的动态分配与MCS的动态选择,并收敛到一种近优的分配策略。其中,GA算法动态地优化ACO算法中的参数配置,ACO算法利用优化后的参数配置执行RB分配与MCS选择。仿真表明,与采用静态参数配置的ACO算法相比较,ACOGA算法可使飞蜂窝系统的吞吐量提高12%以上,并显著提高了收敛速率。

关 键 词:LTE-A  飞蜂窝  MCS选择  资源块分配  QoS保障

QoS-aware Resource Block Allocation and MCS Selection for LTE-A Femtocell Downlink
LI Long-fei,CHEN Xin and XIANG Xu-dong. QoS-aware Resource Block Allocation and MCS Selection for LTE-A Femtocell Downlink[J]. Computer Science, 2015, 42(8): 95-100, 131
Authors:LI Long-fei  CHEN Xin  XIANG Xu-dong
Affiliation:Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, School of Computer, Beijing Information Science and Technology University,Beijing 100101,China,Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, School of Computer, Beijing Information Science and Technology University,Beijing 100101,China and School of Computer and Communication Engineering,Beijing Science and Technology University,Beijing 100083,China
Abstract:We addressed the problem of joint resource block(RB) allocation and modulation-and-coding scheme(MCS) selection for long term evolution-advanced(LTE-A) femtocell downlink.We first formulated the problem as an integer linear program(ILP) whose objective is to maximize the total throughput of a closed femtocell,while guaranteeing minimum throughput for each user.The throughput is one of the most important quality of service(QoS) metrics to measure the network performance.In view of the NP-hardness of the ILP,we then proposed an intelligent optimization algorithm called ACOGA with reduced polynomial time complexity.The proposed ACOGA algorithm applies the genetic algorithm(GA) to optimize the parametric configuration of the conventional ant colony optimization(ACO) algorithm,thereby speeding up the rate of convergence and improving the solution quality.Simulation results show that compared to the conventional ACO algorithm with static parametric configurations,the ACOGA algorithm can improved the system throughput by over 12% and achieves a faster rate of convergence.
Keywords:LTE-A  Femtocell  MCS selection  Resource block allocation  QoS guarantees
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