一种基于接入与回传一体化小基站的用户满意度最大化算法

针对接入与回传一体化小基站场景下用户个性化视频流业务需求问题,该文提出一种基于接入与回传一体化小基站的用户满意度最大化算法。该算法首先根据系统实际可达速率和用户满意度需求速率间的不匹配程度,动态调整下一周期队列传输所需频谱资源,并建立用户质量满意度最大化模型,其次运用Lyapunov优化方法把初始问题转化为Lyapunov偏移加罚项的优化,将溢出概率约束转化为关于自变量的不等式,最后基于拉格朗日对偶分解的用户接入带宽分配算法和基于内点法的回传和接入带宽分配算法进行求解。仿真结果表明,该算法提高了系统用户质量满意度,同时保证了系统稳定性。     

VoLTE业务调度策略优化及实践

为解决4GVoLTE语音业务过程中出现的用户时延增大、MOS值下降、体验变差的问题。通过对SR配置及调度策略的优化、AMC算法优化、SPS调度策略优化等,采用理论仿真和实际测试相结合的方法,验证算法优化前后对系统性能及用户体验的改善效果。本文提出的优化策略一方面保证了VoLTE业务的优先级,另一方面兼顾了VoLTE业务的空口质量,可有效缩短VoLTE业务时延,降低VoLTE业务的空口丢包率,增加基站VoLTE容量,最终达到提升语音质量和用户体验的目的。     

Task scheduling algorithm for system-wide information management based on multiple QoS constraints

An ant colony optimization task scheduling algorithm based on multiple quality of service constraint (QoS-ACO) for SWIM was proposed.Focusing on the multiple quality of service (QoS) requirements for task requests completed in system-wide information management (SWIM),considering the task execution time,security and reliability factors,a new evaluate user satisfaction utility function and system task scheduling model were constructed.Using the QoS total utility evaluation function of SWIM service scheduling to update the pheromone of the ant colony algorithm.The simulation results show that under the same conditions,the QoS-ACO algorithm is better than the traditional Min-Min algorithm and particle swarm optimization (PSO) algorithm in terms of task completion time,security,reliability and quality of service total utility evaluation value,and it can ensure that the user's task scheduling quality of service requirements are met,and can better complete the scheduling tasks of the SWIM.     

面向运营商的P2P流媒体系统架构

针对现有流媒体系统存在的主要问题,本文提出了一种面向电信运营商的可运营、可管理、可扩展的P2P流媒体系统架构,并给出了该架构下流媒体点播业务的实现流程以及主要的资源调度和管理流程.该系统的建立可有效解决困扰业界已久的内容盗版、用户收费、质量保障等关键问题,协助P2P流媒体确立自身盈利模式.     

基于机制设计理论的云计算SLA响应时间优化

针对云计算SLA中响应时间这一衡量云服务质量的重要指标,本文提出了一种DSIC(占优策略激励兼容)机制.在所有云资源提供商都是理性的这一共同知识假定下,DISC机制能保证云资源提供商显示真实的资源成本信息,云服务提供商以此为前提选择预算约束下能够在最短时间内完成用户任务的云资源提供商,从而达到优化用户服务响应时间的目的.本文对该机制的性能进行了严格证明,最后提出了一种寻找最优资源提供商组合的算法.     

联合网络容量与认知用户满意度的资源分配优化方案

机会频谱接入技术是提高频谱利用率的一种有效方法,论文综合考虑信道的时变性、子信道空闲的检测可信度、认知网络与主网络间的互干扰等因素,建立了一种最大化认知网络有效容量和认知用户满意度的多目标优化模型,提出了一种联合资源分配和用户调度的PAUS算法.仿真结果表明,在主用户分布密度较低的环境中,该模型较最大化网络有效容量模型具有更高的用户满意度;较最大化认知用户满意度模型可获得更高的网络有效容量.     

基于效用驱动的网格资源协同预留策略

针对资源预留过程中价格对市场竞争力的影响导致收益不确定性问题,提出一种可量化分析价格、资源竞争力以及收益三者关系的协同预留策略。该策略基于本地任务的真实相关统计特性,在有效保障网格任务QoS与本地任务QoS基础上,通过价格调整来平衡资源提供方的市场竞争力与收益之间的冲突。理论分析给出了预留策略的有效性证明和预留算法,仿真实验采用真实网格系统中任务负载信息作为实验负载,并在模拟网格系统中对预留策略的性能表现进行了检验。实验结果表明,该策略在均衡资源负载、平衡资源节点相对收益率以及保障任务QoS方面的性能表现显著优于传统的预留策略。     

An optimized small-cell planning procedure for Heterogeneous Network to improve network energy efficiency

Cell planning in conventional networks has gained more attention as it directly affects the network performance and deployment cost. Existing cell planning methodologies are framed either with identical base stations or constructing a network without any infrastructure. But heterogeneous networks (HetNets) allow the service provider to deploy small cells over the region to enhance the network performance and signal coverage probability. Thus, a small-cell planning procedure is presented in this research work considering the low-powered base station and deployment cost to enhance the energy efficiency of the HetNet. An adaptive fuzzy expert system is used for cell dimensioning, and a nature-inspired ant colony optimization model is employed for automatic base station placement. Simulation analysis demonstrates that the proposed small-cell planning procedure attains better energy efficiency and user satisfaction ratio compared to conventional planning strategies. In the two cases of simulation analysis, the proposed model attains an average of 85% user satisfaction ratio for case 1 and 87% for case 2, which is better than existing strategies like density-based spatial clustering of applications with noise (DBSCAN), k-means, and number-based spatial clustering (NBSC) algorithms.     

Adaptive resource allocation framework for user satisfaction maximization in multi-service wireless networks

In order to capture and maintain a representative share of the wireless communication market, effective ways to manage the scarce physical resources of cellular networks are fundamental for cellular network operators. In this context, this paper proposes an adaptive Radio Resource Allocation algorithm that targets the user satisfaction maximization in cellular networks with multiple services. The proposed algorithm is mathematically derived from a utility-based cross-layer optimization framework and employs user weights as well as an innovative service weight that is adapted to meet the satisfaction target of the most prioritized service. Furthermore, the proposed algorithm is scalable to several services classes and can be employed in the current and future generations of wireless systems that guarantee orthogonality among the allocable resources. The performance evaluation is conducted in realistic scenarios of the downlink of an Orthogonal Frequency Division Multiple Access based cellular network serving video and Constant Bit Rate flows, where we assume imperfect Channel State Information at the transmitter. Significant gains in the joint system capacity were obtained, demonstrating that the adaptability and service prioritization allow the accomplishment of simultaneously maximizing the user satisfaction for distinct services.     

Development of a Profit-Based Air Cargo Loading Information System

In today's competitive logistics business environment, airfreight forwarders need to optimize every aspect of their logistics operations. However, forwarders still heavily rely on human brain and working experiences for calculating complex cargo packing and scheduling problems. Although recent research studies related to cargo packing and scheduling problems have resulted in the development of a number of advanced techniques of cargo planning, it can be seen that most of the research work is focused on the optimization of space in order to achieve the maximum possible amount of cargo to be packed in the minimum of space. After numerous site evaluation and end-user feedbacks, it is found that space optimization does not necessarily cause profit optimization, which is the ultimate aim of logistics providers. A study of contemporary research publications indicates that there are inadequate research studies related to profit-based optimization in cargo packing areas. This paper presents a profit-based air cargo loading information system (ACLIS) that embeds an innovative technology known as heuristics iterative reasoning technology (HIRT) that supports loading plan generation, focusing on maximization of the profit margin. In general, the proposed system is meant to maximize the profit in the airfreight forwarding business. It adopts an objective function governed by a list of constraints together with rule-based reasoning to provide expert advice to support the generation of appropriate loading plans     

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。     

Scheduling and Resource Allocation Strategy for OFDMA Systems Over Time-Varying Channels

A cross-layer scheduling and resource allocation (SRA) strategy for an adaptive modulation and coding (AMC) based orthogonal frequency multiple access (OFDMA) system is proposed. The objective of this paper is to maximize the system throughput as a function of the bit error rate (BER) and the spectral efficiency based on the selected modulation and coding schemes (MCSs). The proposed strategy contains two main algorithms. Firstly, the scheduling algorithm that aims to maximize the average system throughput by arranging the users in distinct queues according to their priorities and selecting the best user of each queue individually in order to guarantee a fair user service amongst different priority levels. Secondly, the resource allocation algorithm that allocates the user, bit and power based on the channel conditions of the scheduling users and the transmission power constraints. The transmitter of the investigated AMC-OFDMA system at the assigned base station (BS) divides the transmitted OFDMA frame into sub-channels and assigns each sub-channel to a scheduled user. In this paper, we compare the performance of the proposed SRA with the conventional first in first out (FIFO) queuing based scheduling and resource allocation strategies used for an AMC-OFDMA system. The simulation results show that the investigated AMC-OFDMA system based on the proposed SRA strategy outperforms the conventional approaches.     

Control of wireless networks with flow level dynamics under constant time scheduling

We consider a network control problem for wireless networks with flow level dynamics under the general k-hop interference model. In particular, we investigate the control problem in low load and high load regimes. In the low load regime, we show that the network can be stabilized by a regulated maximal scheduling policy considering flow level dynamics if the offered load satisfies a constraining bound condition. Because maximal scheduling is a general scheduling rule whose implementation is not specified, we propose a constant-time and distributed scheduling algorithm for a general k-hop interference model which can approximate the maximal scheduling policy within an arbitrarily small error. Under the stability condition, we show how to calculate transmission rates for different user classes such that the long-term (time average) network utility is maximized. This long-term network utility captures the real network performance due to the fact that under flow level dynamics, the number of users randomly change so instantaneous network utility maximization does not result in useful network performance. Our results imply that congestion control is unnecessary when the offered load is low and optimal user rates can be determined to maximize users’ long-term satisfaction. In the high load regime where the network can be unstable under the regulated maximal scheduling policy, we propose a cross-layer congestion control and scheduling algorithm which can stabilize the network under arbitrary network load. Through extensive numerical analysis for some typical networks, we show that the proposed scheduling algorithm has much lower overhead than other existing queue-length-based constant-time scheduling schemes in the literature, and it achieves performance much better than the guaranteed bound.     

Resource allocation based on cross-layer QoS-guaranteed scheduling for multi-service multi-user MIMO–OFDMA systems

Cross-layer strategies for resource allocation in wireless networks are essential to guaranty an efficient utilization of the scarce resource. In this paper, we present an efficient radio resource allocation scheme based on PHY/MAC cross layer design and QoS-guaranteed scheduling for multi-user (MU), multi-service (MS), multi-input multi-output (MIMO) concept, orthogonal frequency division multiple access (OFDMA) systems. It is about a downlink multimedia transmission chain in which the available resources as power and bandwidth, are dynamically allocated according to the system parameters. Among these parameters, we can mention the physical link elements such as channel state information, spectral efficiency and error code corrector rate, and MAC link variables, which correspond to the users QoS requirements and the queue status. Primarily, we use a jointly method which parametrizes these system parameters, according to the total power, and the bit error rate constraints. Secondly, we propose a QoS-guaranteed scheduling that shares the sub-carriers to the users. These users request several type of traffic under throughput threshold constraints. The main objective in this work is to adjust the average throughput per service of each user, according to their needs and likewise to satisfy a great number of connexions. Subsequently, we consider a model of moderated compartmentalization between various classes of services by partitioning the total bandwidth into several parts. Each class of service will occupy a part of the bandwidth and will be transmitted over a maximum number of sub-carriers. The simulation results show that the proposed strategy provides a more interesting performance improvement (in terms of average data rate and user satisfaction) than other existing resource allocation schemes, such as nonadaptive resource allocation strategy. The performances are also analyzed and compared for the two multi-service multi-user MIMO–OFDMA systems; with sub-carriers partitioning and without sub-carriers partitioning.     

基于社会福利最大化的柔性业务资源分配研究

对经济学方法在无线资源管理中的应用进行了研究,考虑业务、用户、资源等多个域,将无线资源分配看作生产–消费模型,兼顾用户公平性原则,针对不同业务的QoS（quality of service）要求采用不同的资源分配方法,建立了基于社会福利最大化的资源分配模型。采用基于用户柔性业务的调度算法优化所提模型,综合考虑用户效用、网络效益以及运营商收益,实现了基于社会福利最大化的柔性业务资源分配。仿真结果验证了所提算法的优越性。     

Multimedia Service Discrimination Based on Fair Resource Allocation Using Bargaining Solutions

We deal with a resource allocation problem for multimedia service discrimination in wireless networks. We assume that a service provider allocates network resources to users who can choose and access one of the discriminated services. To express the rational service selection of users, the utility function of users is devised to reflect both service quality and cost. Regarding the utility function of a service provider, total profit and efficiency of resource usage have been considered. The proposed service discrimination framework is composed of two game models. An outer model is a repeated Stackelberg game between a service provider and a user group, while an inner model is a service selection game among users, which is solved by adopting the Kalai‐Smorodinsky bargaining solution. Through simulation experiments, we compare the proposed framework with existing resource allocation methods according to user cost sensitivity. The proposed framework performed better than existing frameworks in terms of total profit and fairness.     

Joint Beamforming and Scheduling for a Multi-Antenna Downlink with Imperfect Transmitter Channel Knowledge

We consider the downlink of a wireless system where the base-station has M ges 1 antennas and K user terminals have one antenna each. We study the weighted rate sum maximization in the case of non-perfect Channel State Information at the Transmitter (CSIT). Some relevant downlink optimization problems, such as the stabilization of the transmission queues under random packet arrivals and the proportional fair scheduling for infinite backlogged systems, can be solved as special cases of the proposed problem. We restrict the transmitter strategy to be based on Gaussian coding and beamforming. Even under this simplifying condition, the problem at hand is non-convex and it does not appear to lend itself to a simple algorithmic solution. Therefore, we introduce some approximations that yield a definition of signal-to-interference plus noise ratio (SINR) commonly used in the classical array- processing/beamforming literature. For the simpler (but still non-convex) approximated problem, we propose a powerful heuristic solution based on greedy user selection and a gradient iteration that converges to a local maximum of the objective function. This method yields very competitive results with relatively low computational complexity. Extensive simulations show that, in the case of perfect CSIT, the proposed heuristic scheme performs very closely to the optimal (dirty-paper coding) strategy while, in the case of non-perfect CSIT, it significantly outperforms previously proposed suboptimal approaches, such as random beamforming and approximated zero-forcing with greedy user selection.     

非对称毫米波大规模MIMO系统信道特性研究

在大规模多输入多输出（multiple-input multiple-output,MIMO）系统中,合理的天线选择、用户调度以及用户功率分配方案,对提升系统能效、节省资源成本有着重要的作用. 针对大规模MIMO下行链路通信场景,基于能效最大化准则,提出了一种联合天线选择、用户调度以及功率分配的低复杂度优化算法. 首先,针对天线选择和用户调度问题,结合递增递减的选择思想,以最大化系统能效为目标,对天线和用户进行双向交替搜索;其次,对于搜索过程中的用户功率分配问题,采用分式规划理论和拉格朗日对偶算法得到最优能效功率的闭式解,三个参数进行迭代优化,从而得到系统最优能效. 仿真结果表明,本文所提算法不仅具有低复杂度而且具有较好性能,能够有效降低大规模MIMO系统的能耗.

     

Low Complexity User Scheduling,Ordering and Transmit Covariance Matrix Optimization Algorithms for Successive Zero-Forcing Precoding

In this paper we consider user scheduling, ordering and transmit covariance matrix optimization problems under successive zero-forcing (SZF) precoding for multiuser multiple-input multiple-output downlink. We propose a heuristic user scheduling metric and an intermediate user grouping technique to develop a low complexity greedy scheduling algorithm. A suboptimal user ordering technique is also proposed for transmit covariance matrix optimization under SZF. Proposed algorithm is of low complexity, but performs closely to the highly complex exhaustive search algorithm. For transmit covariance optimization under SZF, a dirty paper coding based algorithm has been previously proposed, which is computationally very complex. In this paper, we propose a suboptimal but much simplified algorithm, which employs an iterative procedure similar to a known multiple access channel (MAC) covariance optimization algorithm, but does not involve multiple levels of covariance matrix transformations. With the proposed suboptimal user ordering the exhaustive search through all possible user orders is avoided during transmit covariance matrix optimization resulting in a significant complexity reduction, and without a significant performance penalty. Simulation results show that the proposed algorithm performs very close to the known algorithm in the low SNR region.     

云环境下面向跨域作业的调度方法

云环境下,因数据局部性或是任务对资源的特殊偏好,一个作业所包含的任务往往需要在不同的数据中心局点上运行,此类作业称为跨域作业.跨域作业的完成时间取决于最慢任务的执行效率,即存在木桶效应.针对各域资源能力异构条件下不合理的调度策略导致跨域作业执行时间跨度过长的问题,本文提出一种面向跨域作业的启发式调度方法MIN-Max-Min,优先选择期望完成时间最短的作业执行.通过实验表明,与先来先服务的策略相比,该方法能将跨域作业平均执行时间跨度减少40%以上.