基于OFDMA/TDM系统的跨层资源分配算法

研究了OFDMA/TDM系统的资源分配问题,基于最差信道用户优先原则,提出了一种新的跨层资源分配算法CRRA(Cross-layer Radio Resource Allocation)及其简化算法S-CRRA(Simplified CRRA).该算法利用用户数据队列长度动态变化的特性和多用户分集策略以实现在满足用户服务质量(Quality of Service,QoS)的基础上最大化系统吞吐量,并且将频谱和功率分配分开进行以降低计算复杂度.仿真结果表明,CRRA算法和S-CRRA算法在系统吞吐量上基本一致,两者较已有的OFDMA/TDM系统资源分配算法能提高10%-24%的系统吞吐量.     

基于 QoS 保障的密集 WLAN 多维资源分配算法

针对密集无线局域网覆盖环境,以提升网络性能为目标,提出了一种多维无线资源分配方法。通过网络覆盖衡量参数、干扰衡量参数和QoS衡量参数联合评估网络性能,建立了网络性能与资源分配向量之间的优化目标函数,进而采用禁忌搜索的方法求解目标函数后得到最优多维资源分配向量,包括所用AP、信道和功率分配向量。仿真结果表明,所提算法提升了网络性能,更好地保证了用户QoS需求。     

工业物联网中基于任务紧急程度的资源分配算法

工业物联网中任务的生成通常具有连续性和周期性,并且任务对时延要求很高,这给系统成本带来了挑战。为应对这一挑战,提出了一种基于任务紧急程度的成本最小化资源分配算法。通过遗传算法优化任务的卸载策略和系统的资源分配策略,对于卸载的任务,根据任务的紧急程度进行调度,并在满足时延要求的前提下计算任务的最优发射功率。仿真结果表明,所提算法有效改善了系统总能耗成本。     

基于模糊Topsis的传感器资源分配算法

影响传感器资源分配的目标属性有多种.文中将目标模糊多属性效用作为传感器资源分配的依据,提出了一种基于模糊Topsis的传感器资源分配算法,并分别给出了影响资源分配的目标多属性模糊效用函数.提出了几种传感器资源分配性能评价指标.仿真结果表明,该算法充分考虑了目标跟踪和威胁等多因素,更符合不同目标对传感器资源的需求.     

基于无线中继网络的OFDMA-MIMO资源分配算法研究

无线中继技术与OFDMA-MIMO技术相结合,能够大大提高系统的性能,是一种极具潜力的技术。本文结合无线中继网络中OFDMA的帧结构,以轮询调度算法和贪婪算法为参考,提出了多天线下的OFDMA无线中继网络的比例公平资源分配算法;并和无中继的OFDMA系统及单天线下的OFDMA无线中继网络资源分配算法相比较。通过仿真表明本文提出的资源分配算法在考虑每个用户数据速率的同时能够大大提高系统的传输速率,满足用户的多种业务需求。     

基于射频能量收集的无线中继网络资源分配

针对现有射频能量收集网络资源分配研究局限于单个数据源场景,无法适配于多数据源网络的问题,提出了一种适用于多数据源场景的射频能量收集中继网络传输协议框架,在该框架内节点可作为源节点或中继节点传输自身数据或转发数据,并在其他节点的数据传输过程中完成射频能量收集。以协议框架为基础,分别以系统吞吐量及用户公平性为优化目标设计两种资源分配方案。仿真表明,两种方案可有效改善网络吞吐量及资源分配公平性。     

基于Stackelberg博弈的无线资源分配算法

4G系统可以随意更改速率控制方案以满足网络应用的需要,这样在传输协议中系统不遵守速率控制规则的行为更使得4G系统能耗大大提高。针对此4G系统接入选择问题,结合单跟随者Stackelberg博弈模型,文章提出了一种使用简单的效用函数和线性代价函数的4G系统无线资源分配算法,理论证明了其收敛的特性,而且通过仿真对基于Stackelberg博弈的4G系统无线资源分配算法的运行结果进行评价,证明了该算法在纳什均衡态的稳定性,取得了4G系统的最优传输效能策略和子博弈完美纳什均衡。     

用于多宿容迟移动网络的实时资源分配算法

多宿容迟移动网络适用于物流业物联网等误码率高、频繁中断的环境.然而由于其多宿的特点,其资源分配复杂,难以保证实时性.针对接纳控制过程的资源分配,以效用差分法设计资源分配算法,能够保证过程中的每一步效用单调增加,同时该算法时间复杂度降低到 O (n),可以保证实时性.最后将容迟网络协议框架代码 DTN2和 LTP 植入网络模拟软件 NS2,就节点稀疏的场景进行仿真.实验结果说明算法能够提供较好性能,并同时具有良好的实时性.     

基于Stackelberg博弈的虚拟化无线传感网络资源分配策略

虚拟化技术可有效缓解当前无线传感网络(WSN)中资源利用率较低、服务不灵活的问题。针对虚拟化WSN中的资源竞争问题,该文提出一种基于Stackelberg博弈的多任务资源分配策略。依据所承载业务的不同服务质量(QoS)需求,量化多个虚拟传感网络请求(VSNRs)的重要程度,进而,利用分布式迭代方法,获取WSN的最优价格策略和VSNRs的最优资源需求量,最后,根据纳什均衡所确定的最优价格、最优资源分配量,对多个VSNRs分配资源。仿真结果表明,所提策略不仅能满足用户的多样化需求,而且提升了节点和链路资源利用率。

     

基于网络切片的网络效用最大化虚拟资源分配算法

为了实现网络资源的动态分配,提高网络资源利用率,满足用户业务多样性带来的切片网络差异需求,该文提出一种基于网络效用最大化的虚拟资源分配算法。该算法采用商业化模式将频谱资源作为收益载体,并对不同切片网络进行差异化定价。同时将计算资源和回程链路作为开销,还考虑了切片网络对计算资源和频谱资源的差异性需求,最后以最大化网络收益建立效用模型。并通过拉格朗日对偶分解设计了分布式迭代算法对效用模型进行求解。仿真结果表明,该算法提高了服务用户比例,并使得网络资源获得最大收益。     

基于非线性服务曲线的RSVP资源分配方式

本文主要针对基于RSVP的保证服务，讨论了如何利用非线性服务曲线进行资源分配，以实现带宽和时延要求的解耦。提出了一种简单有效的非线性服务曲线，数值分析结果表明就资源利用率而言，其性能远远优于传统的线性服务曲线。最后论文讨论了未来的发展趋势并给出了有待研究的一些课题。     

Robust resource allocation algorithm for heterogeneous wireless networkwith SWIPT

To improve energy efficiency and robustness of heterogeneous wireless networks with wireless information and power transfer,the robust joint transmit power and power splitting resource allocation problem was studied.Based on mini-max probability machine and Dinkelbach method,the original NP-hard problem was transformed into a solvable convex optimization form,meanwhile a distributed dual resource allocation algorithm was proposed.Additionally,both computational complexity and robust sensitivity were analyzed.Simulation results show that the proposed algorithm can guarantee the quality of service requirements of macro cellular users and femtocell users under channel uncertainties.     

无线网络虚拟化中资源共享的功率分配算法

针对传统无线网络中功率不能动态分配共享的问题,采用无线网络虚拟化,设计了一种基于博弈的两阶段功率分配方法(G2SPA, game theory based two steps power allocation scheme for wireless network virtualization),首先利用买卖博弈模拟了服务提供商(SP, service provide)和移动用户(MUE, mobile user equipment)之间的相互影响,提出了基于斯坦博格均衡(SE, stackelberg equilibrium)的报价策略。然后,利用拍卖理论对空闲下行功率资源进行再分配,采取McAfee机制保证拍卖的诚实性。通过仿真实验证明G2SPA算法的正确性和有效性。     

无线EH认知物联网网络资源分配研究

由于5G技术的快速发展,基于能量采集的认知物联网(Internet of things,IOT)具有很大的应用前景.针对无线物联网网络中存在的"doubly-near-far"问题,考虑在认知物联网网络中将能量采集与信息接收站分开管理,提出了一个基于能量采集的异构认知物联网网络模型,实现用户能效的最大化.此外,该模型不...     

Dynamic resource allocation in cloud download service

Cloud download service, as a new application which downloads the requested content offline and reserves it in cloud storage until users retrieve it, has recently become a trend attracting millions of users in China. In the face of the dilemma between the growth of download requests and the limitation of storage resource, the cloud servers have to design an efficient resource allocation scheme to enhance the utilization of storage as well as to satisfy users' needs like a short download time. When a user's churn behavior is considered as a Markov chain process, it is found that a proper allocation of download speed can optimize the storage resource utilization. Accordingly, two dynamic resource allocation schemes including a speed switching (SS) scheme and a speed increasing (SI) scheme are proposed. Both theoretical analysis and simulation results prove that our schemes can effectively reduce the consumption of storage resource and keep the download time short enough for a good user experience.     

Resource allocation for statistical quality of service provision in buffered crossbar switches

The buffered crossbar switch is a promising switching architecture that plays a crucial role for providing quality of service (QoS) in computer networks. Sufficient amount of resources—bandwidth and buffer space—must be allocated in buffered crossbar switches for QoS provision. Resource allocation based on deterministic QoS objectives might be too conservative in practical network operations. To improve resource utilization in buffered crossbar switches, we study the problem of resource allocation for statistical QoS provision in this paper. First, we develop a model and techniques for analyzing the probabilistic delay performance of buffered crossbar switches, which is described by the delay upper bound with a prescribed violation probability. Then, we determine the required amounts of bandwidth and buffer space to achieve the probabilistic delay objectives for different traffic classes in buffered crossbar switches. In our analysis, we apply the effective arrival envelope to specify traffic load in a statistical manner and characterize switch service capacity by using the service curve technique. Instead of just focusing on one specific type of scheduler, the model and techniques developed in this paper are very flexible and can be used for analyzing buffered crossbar switches with a wide variety of scheduling algorithms. Copyright © 2007 John Wiley & Sons, Ltd.     

An elastic resource allocation algorithm enabling wireless network virtualization

Following the wired network virtualization, virtualization of wireless networks becomes the next step aiming to provide network or infrastructure providers with the ability to manage and control their networks in a more dynamic fashion. The benefit of the wireless mobile network virtualization is a more agile business model where virtual mobile network operators (MNOs) can request and thus pay physical MNOs in a more pay‐as‐you‐use manner. This paper presents some resource allocation algorithms for joint network virtualization and resource allocation of wireless networks. The overall algorithm involves the following two major processes: firstly, to virtualize a physical wireless network into multiple slices, each representing a virtual network, and secondly, to carry out physical resource allocation within each virtual network (or slice). In particular, the paper adopts orthogonal frequency division multiplexing (OFDM) as its physical layer to achieve more efficient resource utilization. Therefore, the resource allocation is conducted in terms of sub‐carriers. Although the motivation and algorithm design are based on IEEE 802.16 or WiMAX networks, the principle and algorithmic essence are also applicable to other OFDM access‐based wireless networks. The aim was to achieve the following design goals: virtual network isolation and resource efficiency. The latter is measured in terms of network throughput and packet delivery ratio. The simulation results show that the aforementioned goals have been achieved. Copyright © 2012 John Wiley & Sons, Ltd.     

Deep reinforcement learning based resource allocation algorithm in cellular networks

In order to solve multi-objective optimization problem,a resource allocation algorithm based on deep reinforcement learning in cellular networks was proposed.Firstly,deep neural network (DNN) was built to optimize the transmission rate of cellular system and to complete the forward transmission process of the algorithm.Then,the Q-learning mechanism was utilized to construct the error function,which used energy efficiency as the rewards.The gradient descent method was used to train the weights of DNN,and the reverse training process of the algorithm was completed.The simulation results show that the proposed algorithm can determine optimization extent of optimal resource allocation scheme with rapid convergence ability,it is obviously superior to the other algorithms in terms of transmission rate and system energy consumption optimization.     

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。