基于QoS保障的LTE上行分组调度算法

有效分配有限的无线资源以提高系统的吞吐量,同时满足不同业务的服务质量(QoS)需求是LTE通信系统的关键技术之一。结合比例公平算法(PF)和用户满意度的公平算法(USGF)提出了一种基于速率需求满足程度矩阵的分组调度算法,根据速率需求满足程度矩阵改变判决因素以获取更高的吞吐量。仿真结果表明,该算法能在不降低用户满意度的条件下提高系统的吞吐量。     

基于多业务QoS的LTE下行资源分配算法

提出一种保证混合业务QoS的LTE系统下层资源分配算法。该算法采用跨层设计思想,根据用户缓存队列状态以及QoS需求将用户按照调度的紧急程度分为三类,然后按照不同的调度准则依次为这三类用户分配资源块。仿真结果表明,所提资源块分配算法不仅能够大幅度降低用户的丢比特率,还能改善用户公平性和吞吐量性能。     

多业务MIMO-OFDMA/SDMA系统跨层调度与动态资源分配

对多业务MIMO-OFDMA/SDMA 系统下行链路跨层调度与动态资源分配问题进行了研究.首先,在满足各种约束条件的前提下,以最大化系统吞吐量为目标建立了相应的优化模型;然后,提出了一种基于业务类型和子空间距离的用户分组算法,该算法采用聚类分析的方法在每个子载波上对配置有多根接收天线的用户进行分组,从而降低了调度时所需搜索的用户空间的维数;接着,基于所提出的用户分组算法并结合不同业务的优先级提出了一种新的跨层调度和资源分配算法,该算法充分利用跨层信息为每个子载波调度相应的用户组,并为调度到的用户分配相应的系统资源,从而通过最大化每个子载波的吞吐量近似实现了系统整体吞吐量的最大化.仿真结果表明,与现有的方案相比,所提算法更好地满足了不同业务用户的QoS要求,并获得了更好的吞吐量性能.     

HSDPA系统中一种感知用户终端缓存状态的QoE保障调度算法

针对HSDPA系统中现有调度算法无法满足实时业务QoE的缺点,提出一种保障实时业务QoE的调度算法。该算法根据用户反馈的信道质量信息和在基站获取到的用户终端缓存状况信息。确定用户的优先级并据此调度优先级最高的用户,进而保证实时业务的吞吐量和QoE需求。仿真结果表明,与轮询调度算法、比例公平调度算法相比,提出的调度算法不仅能够保证实时业务的QoE需求,而且能满足非实时业务基本的吞吐量需求。     

一种用于MIMO-OFDMA系统的调度和资源分配算法

该文提出了一种用于MIMO-OFDMA 系统下行链路的调度和资源分配算法,该方法能够优化利用空时频三维资源,为不同类业务提供QoS保证。该算法不仅结合了先进的物理层技术,同时从媒体接入控制(MAC)层考虑了业务特性、QoS 需求以及用户公平性等因素对资源进行分配。仿真结果表明,该算法在保证多业务传输质量的同时提高系统吞吐量。     

TD-HSUPA无线分组调度算法研究

首先介绍了在TD-HSUPA系统中调度算法对无线资源管理的重要影响及Node B调度的基本原理.然后分析了几种常用的无QoS保证的调度算法和两种保证多业务QoS的调度算法,并通过公平性、吞吐量等性能指标对它们进行分析比较.最后引入了经济学中的效用函数思想,分析了基于效用函数的调度算法,它能够结合相应业务的QoS要求,更加真实地反应用户的满意情况,为调度算法的改进提供依据,从而使网络能够承载更高的数据服务容量,增强网络建设的有效性.     

提供QoS保证的比例公平调度改进算法及其应用

优化分配有限的无线资源以提高系统容量,同时为不同用户的不同业务提供服务质量保障,是未来无线通信的关键问题之一.传统的比例公平(PF)算法是系统吞吐量与公平性的折中,没有考虑用户的QoS需求.对传统的PF算法作一些改进,根据用户的目标速率设置不同的加权值.根据时延设置不同的历史平均窗长,以提供一定的QoS保证;并分析了该算法在多天线多载波系统下行链路中的性能.仿真结果表明改进的PF算法能根据用户需求提供QoS保证,同时获得多用户分集增益.     

3GPP LTE系统中无线视频传输调度算法研究

摘要：针对3GPP LTE系统,本文提出了适用于下行链路视频业务的一种新的分组调度算法,即时延优先比例公平调度（Delay First-Proportional Fair Scheduling,DF-PFS）。当需要做出调度决策时,该算法利用每个用户的数据包时延信息和瞬时下行信道条件,在满足用户QoS前提下最大限度地提高系统吞吐量。同时,当用户选择资源块（RB）进行传输后,即从用户集合中将该用户删除,避免接近eNodeB的用户一直占用无线资源,确保了资源分配的公平性。实验仿真结果表明,该算法在丢包率和PSNR性能上优于最大权重时延优先（M-LWDF）算法,在保证用户间公平性前提下,满足了视频业务的QoS要求。     

一种基于QoS下行资源调度的改进算法

针对LTE系统中用户无线承载QoS要求的不同,提出了一种考虑用户QoS的下行链路资源动态调度改进算法.改进算法在比例公平算法的基础上引入承载的QoS权重值,通过计算出的承载调度值的大小来决定调度承载的顺序.仿真结果表明,改进调度算法可以有效地满足承载的QoS要求,同时保证了用户间的公平性和系统的吞吐量.     

正交频分多址系统中一种面向多业务应用的自适应资源分配算法

针对正交频分多址(OFDMA)系统下行链路多业务自适应调度的问题,该文首先以最大化系统吞吐量为优化目标、每种业务的服务质量(QoS)保证为约束条件,建立了一种通用的多业务自适应资源分配模型。为解决此优化问题,提出了一种具体的自适应资源调度算法。该算法对实时业务按照用户选择最好的信道的原则分配尽可能少的资源以保证其QoS,对非实时业务把尽可能多的剩余资源按照信道选择最好的用户的原则进行分配,充分利用信道资源,提升系统容量。仿真结果表明,该算法保证了下行OFDMA系统吞吐量的同时,在实时业务的延时和丢包率等方面有一定的优越性。     

基于用户画像与关联规则的全域电力资源调度算法

为解决应用调度算法进行全域电力资源调度,资源剩余率依旧较高的问题,提出结合用户画像与关联规则的新型调度算法,实现全域电力资源的合理分配。运用双聚类算法,对整个调度区域内所有用户用电数据进行分析,构建电力用户画像从而描述用户用电个性化需求;以用户画像为基础,建立以满足用户需求为核心的全域资源分配模式;总结全域内资源调度子任务,计算不同子任务之间的支持度和置信度,结合关联规则实现子任务的分组;根据子任务组进行资源分域,在每个分域中设置二级调度中心,再与全域一级调度中心相连接,实现全域资源集中调度。实验结果表明,所提调度算法应用后,电力测试系统每日的全域资源剩余率出现了大幅降低,仅保持在12%左右。该算法具有较好的实际应用价值。     

A Novel Scheduling Algorithm for Low and High Mobility Users in Multi-hop WiMAX Network

QoS provisioning and high capacity for high mobility users are considered as the distresses of broadband wireless communications (BWC) and specifically the key technology of WiMAX. Hence, the scheduling and resource allocation algorithms play the main role in this regard. In the research conducted on scheduling algorithms in WiMAX network, two principal methods of AMC and PUSC are used. The high capacity in AMC mode algorithms is achieved by considering the low speed users. Conversely, in PUSC mode algorithms, speed does not affect the network performances; however, the capacity is low. To date, the importance of presenting QoS and maintaining the network capacity for the users with different speeds has not been acknowledged yet. This paper presents novel scheduling algorithms and also new frame partitioning scheme which are proper for the users with different mobility speeds. The new algorithm uses two modes of AMC and PUSC simultaneously to maintain the high capacity of the network. QoS is also provided. The simulation results reveal that our algorithm increases capacity while it presents low packet delay and packet loss rate in the presence of both high and low mobility speed users.     

HSDPA分组调度算法与小区吞吐率研究

分组调度是HSDPA的核心技术之一,对网络性能有重要影响。在HSDPA分组调度功能和实现的基础上,重点分析对比3种典型分组调度算法原理及其对系统的影响,并通过实际测试验证,明确了不同调度算法对小区吞吐率的影响。结论:MAXCI算法下能够得到最大的系统吞吐量,公平性最差;RR算法公平性最好,系统资源利用率最低,吞吐率最小;EPF算法既考虑了用户的公平性,也能从一定程度上保证比较高的系统吞吐量,是一种实用的调度方法。     

云环境下任务调度算法对比研究

云计算是完全基于互联网的新兴技术。云计算环境中的任务调度问题一直都是该领域的研究热点。合理高效的任务调度算法在云环境中能有效的缩短任务完成时间,提高系统负载均衡,更好的满足用户与云提供商的需求。本文研究了云平台的任务调度机制,探究了任务调度过程中的关键性指标。通过云仿真平台CloudSim实现并分析了顺序调度算法、Min-Min算法和Max-Min算法,对比其在随机生成用户任务负载与虚拟机计算资源的情况下的任务完成时间,实验证明Min-Min算法与Max-Min算法均优于顺序调度算法。以此为未来研究提供实验支撑和方向。     

基于HSDPA的信道相关分组调度算法研究

针对HSDPA(高速下行分组接入)系统中几种支持非实时业务的经典分组调度算法Max C/I(最大载干比)和PF(正比公平)算法缺乏系统公平性的问题,提出一种基于HSDPA的快速公平分组调度算法。此算法在保证信道瞬时条件和系统吞吐量的前提下,旨在为那些平均吞吐量低于某一阈值的用户提供优先被服务的机会。仿真结果表明,此算法较之Max C/I和PF算法能够保证用户间的长期公平性。     

Opportunistic packet scheduling algorithm for non-real-time service with a soft QoS requirement in a mobile broadband wireless access system

In this paper, we present a packet scheduling algorithm for a non-real-time service, with soft QoS requirements, which allows for degrading the QoS level, e.g., typically the packet delay, whenever necessary, in mobile broadband wireless Internet access systems. This algorithm is designed to properly trade off system throughput and delay performance, which can improve the system capacity by relaxing the delay constraint with respect to the underlying soft QoS requirement. This is as opposed to most of the existing packet scheduling algorithms for non-real-time service which are simply designed to maximize the system throughput without a delay constraint. The proposed adaptive exponential scheduling algorithm intentionally introduces additional delay to some users, especially under bad channel conditions, opportunistically allowing for serving users only under good channel conditions, as long as the resulting QoS degradation is acceptable for non-real-time service users. The results from a system-level simulation demonstrate that the system capacity can be significantly increased over existing algorithms, by as much as 65%, using the adaptive exponential scheduling algorithm while satisfying the given QoS-level requirements.     

Opportunistic Scheduling with QoS Constraints for Multiclass Services HSUPA System

This paper focuses on the scheduling problem with the objective of maximizing system throughput, while guaranteeing long‐term quality of service (QoS) constraints for non‐realtime data users and short‐term QoS constraints for realtime multimedia users in multiclass service high‐speed uplink packet access (HSUPA) systems. After studying the feasible rate region for multiclass service HSUPA systems, we formulate this scheduling problem and propose a multi‐constraints HSUPA opportunistic scheduling (MHOS) algorithm to solve this problem. The MHOS algorithm selects the optimal subset of users for transmission at each time slot to maximize system throughput, while guaranteeing the different constraints. The selection is made according to channel condition, feasible rate region, and user weights, which are adjusted by stochastic approximation algorithms to guarantee the different QoS constraints at different time scales. Simulation results show that the proposed MHOS algorithm guarantees QoS constraints, and achieves high system throughput.     

On the Convergence of Genetic Scheduling Algorithms for Downlink Transmission in Multi-User MIMO Systems

In a multi-user MIMO system using a successive precoding method such as dirty paper coding, it is combinatorially complex to determine the optimal set of users to schedule and the proper order to encode their signals in order to optimize a utility function in a scheduling algorithm. Genetic algorithms represent a fast suboptimal approach to reducing the complexity of the search. In this paper, we build upon prior work that implements scheduling via genetic algorithms. We examine the impact of parameter values within the adaptive mutation rate of the algorithm on its convergence time. We demonstrate that although there is a range of values for the parameters that yields similar near-minimum convergence times, it is nonetheless important to ensure that the parameters are tuned to be within that range. In one case, tuning the parameter values reduces the time of convergence to less than 30% compared to that achievable with the initial parameter values. We also demonstrate that the proper parameter values are dependent on both the number of transmit antennas and the number of users in the pool of users to be scheduled. A simple equation is proposed that is linear in the adaptive mutation parameters to tune the values for different numbers of transmit antennas and users.     

QoS‐aware LTE‐A downlink scheduling algorithm: A case study on edge users

4G/LTE‐A (Long‐Term Evolution—Advanced) is the state of the art wireless mobile broadband technology. It allows users to take advantage of high Internet speeds. It makes use of the OFDM technology to offer high speed and provides the system resources both in time and frequency domain. A scheduling algorithm running on the base station holds the allocation of these resources. In this paper, we investigate the performance of existing downlink scheduling algorithms in two ways. First, we look at the performance of the algorithms in terms of throughput and fairness metrics. Second, we suggest a new QoS‐aware fairness criterion, which accepts that the system is fair if it can provide the users with the network traffic speeds that they demand and evaluate the performance of the algorithms according to this metric. We also propose a new QoS‐aware downlink scheduling algorithm (QuAS) according to these two metrics, which increases the QoS‐fairness and overall throughput of the edge users without causing a significant degradation in overall system throughput when compared with other schedulers in the literature.     

Switched-Diversity Approach for Cognitive Scheduling

This article investigates the scheduling of secondary users in a spectrum-sharing cognitive environment under the primary user’s outage probability constraint. A switched-diversity combining approach to schedule the secondary users is explored. Specifically, switch-and-examine, switch-and-stay, selection-combining, and post-selection scheduling algorithms are investigated. Secondary users’ average performance measures are derived for the scheduling algorithms and compared against those of a single-user cognitive system. Results of this work illustrate the trade-off between the complexity of a scheduling algorithm and its average performance.