Resource Planning and Allocation Problem Under Uncertain Environment

This paper generalizes the classic resource allocation problem to the resource planning and allocation problem, in which the resource itself is a decision variable and the cost of each activity is uncertain when the resource is determined. The authors formulate this problem as a two-stage stochastic programming. The authors first propose an efficient algorithm for the case with finite states.Then, a sudgradient method is proposed for the general case and it is shown that the simple algorithm for the unique state case can be used to compute the subgradient of the objective function. Numerical experiments are conducted to show the effectiveness of the model.     

Distributed Nonsmooth Convex Optimization over Markovian Switching Random Networks with Two Step-Sizes

This paper investigates the distributed convex optimization problem over a multi-agent system with Markovian switching communication networks. The objective function is the sum of each agent's local nonsmooth objective function, which cannot be known by other agents. The communication network is assumed to switch over a set of weight-balanced directed graphs with a Markovian property. The authors propose a consensus sub-gradient algorithm with two time-scale step-sizes to handle the Markovian switching topologies and the absence of global gradient information. With proper selection of step-sizes, the authors prove the almost sure convergence of all agents' local estimates to the same optimal solution when the union graph of the Markovian network' states is strongly connected and the Markovian chain is irreducible. The convergence rate analysis is also given for specific cases.Simulations are given to demonstrate the results.     

基于时间反演的上行NOMA系统能效优化算法

为了解决上行非正交多址接入(non-orthogonal multiple access,NOMA)系统在多径环境下传输效率较低问题,提出了一种基于时间反演(time reversal,TR)的上行NOMA网络资源分配算法.首先,利用TR技术独特的空时聚焦特性,增大信号的接收强度.其次,考虑用户最小传输速率约束和用户最...     

Convergence of Distributed Gradient-Tracking-Based Optimization Algorithms with Random Graphs

This paper studies distributed convex optimization over a multi-agent system, where each agent owns only a local cost function with convexity and Lipschitz continuous gradients. The goal of the agents is to cooperatively minimize a sum of the local cost functions. The underlying communication networks are modelled by a sequence of random and balanced digraphs, which are not required to be spatially or temporally independent and have any special distributions. The authors use a distributed gradient-tracking-based optimization algorithm to solve the optimization problem. In the algorithm,each agent makes an estimate of the optimal solution and an estimate of the average of all the local gradients. The values of the estimates are updated based on a combination of a consensus method and a gradient tracking method. The authors prove that the algorithm can achieve convergence to the optimal solution at a geometric rate if the conditional graphs are uniformly strongly connected, the global cost function is strongly convex and the step-sizes don't exceed some upper bounds.     

云边协同系统中基于博弈论的资源分配与任务卸载方案

综合考虑时延、能耗和计算资源成本，构建云边协同系统中的效用最大化问题，并将其分解为计算资源分配、上行功率分配和任务卸载策略三个子问题。提出一种基于博弈论的资源分配和任务卸载方案（game-based resource allocation and task offloading， GRATO） 以分别解决上述子问题。利用凸优化条件求得计算资源分配最优解；设计一种低复杂度的上行功率分配方法用于降低无线干扰；针对任务卸载策略优化问题，提出一种基于博弈论的分布式任务卸载算法（game-based distributed task offloading algorithm， GDTOA）。仿真结果表明，GRATO方案在时延和能耗方面的性能优于其他方案，还可以感知用户的优先级，使紧急用户具有更高的效用和更低的时延。     

Algorithm Implementation for Distributed Convex Intersection Computation

Intersection computation of convex sets is a typical problem in distributed optimization. In this paper, the algorithm implementation is investigated for distributed convex intersection computation problems. In a multi-agent network, each agent is associated with a convex set. The objective is for all the agents to achieve an agreement within the intersection of the associated convex sets. A distributed"projected consensus algorithm" is employed, and the computation of the projection term is converted to a constrained optimization problem. The solution of the optimization problem is determined by Karush-Kuhn-Tucker(KKT) conditions. Some implementable algorithms based on the simplex method are introduced to solve the optimization problem. Two numerical examples are given to illustrate the effectiveness of the algorithms.     

单播与多速率组播共存网络中的公平资源分配

以最大化网络用户效用值为目标,考虑组播接收者的异构性,将单播和组播任务纳入统一的凸规划模型研究,并应用惩罚函数法推导出一种启发式速率控制算法。该算法在IP网络中分布式实现时,路由器使用RED算法标记报文,反馈拥塞信息;用户端提取拥塞信息计算最优速率,并作相应更新。通过选择合适的算法参数及效用函数,单播与组播将依据比例公平性准则共享网络资源。仿真结果验证了该算法的有效性。另外,还分析了多速率组播分层实现时,不同的带宽离散化密度对资源分配公平性的影响。     

Scenario-based stochastic resource allocation with uncertain probability parameters

The stochastic resource allocation (SRA) problem is an extensive class of combinatorial optimization problems widely existing in complex systems such as communication networks and unmanned systems. In SRA, the ability of a resource to complete a task is described by certain probability, and the objective is to maximize the reward by appropriately assigning available resources to different tasks. This paper is aimed at an important branch of SRA, that is, stochastic SRA (SSRA) for which the probability for resources to complete tasks is also uncertain. Firstly, a general SSRA model with multiple independent uncertain parameters (GSSRA-MIUP) is built to formulate the problem. Then, a scenario-based reformulation which can address multi-source uncertainties is proposed to facilitate the problem-solving process. Secondly, in view of the superiority of the differential evolution algorithm in real-valued optimization, a discrete version of this algorithm was originally proposed and further combined with a specialized local search to create an efficient hybrid optimizer. The hybrid algorithm is compared with the discrete differential evolution algorithm, a pure random sampling method, as well as a restart local search method. Experimental results show that the proposed hybrid optimizer has obvious advantages in solving GSSRA-MIUP problems.     

一种认知无线电资源分配系统级仿真平台

研究了认知无线电资源分配技术的系统级仿真平台的设计与实现,该平台采用多PC机组网的半实物仿真形式,提供了对认知无线电资源分配技术进行系统级评估的途径。给出了平台的设计架构,以及设计和实现中的关键技术,并对平台的通用功能进行了验证。此外,针对一种认知无线电子信道及功率联合分配算法,详述了该算法在平台上实现的过程,并对其性能进行了仿真分析。     

An Asset Allocation Model and Its Solving Method

Asset allocation is an important issue in finance, and both risk and return are its fundamental ingredients. Rather than the return, the measure of the risk is complicated and of controversy.In this paper, we propose an appropriate risk measure which is precisely a convex combination of mean semi-deviation and conditional value-at-risk. Based on this risk measure, investors can trade-off flexibly between the volatility and the loss to tackle the incurring risk by choosing different convex coefficients.As the presented risk measure contains nonsmooth term, the asset allocation model based on it is nonsmooth. To employ traditional gradient algorithms, we develop a uniform smooth approximation of the plus function and convert the model into a smooth one. Finally, an illustrative empirical study is given. The results indicate that investors can control risk efficiently by adjusting the convex coefficient and the confidence level simultaneously according to their perceptions. Moreover, the effectiveness of the smoothing function proposed in the paper is verified.     

针对目标跟踪的分布式MIMO雷达资源分配算法

基于分布式多输入多输出雷达,针对目标跟踪精度的优化问题提出了一种联合资源优化分配算法。首先,推导了机动目标跟踪误差的贝叶斯克拉美罗下界(Bayesian Cramer Rao lower bound, BCRLB),由BCRLB可知其跟踪精度主要由信号发射功率、带宽和信号有效时宽决定。然后,以最小化目标的BCRLB为目标函数,建立了包含相应的3个资源变量的优化模型,分析可知该模型的求解是一个非凸问题的求解。所以采用循环最小化算法和凸松弛的方法将这个非凸的优化模型转化为凸优化模型进行求解。最后,仿真结果表明,利用所提出的资源分配算法能明显提高机动目标的跟踪精度。     

Resource Allocation Based on DEA and Non-Cooperative Game

Resource allocation is one of the most important applications of data envelopment analysis (DEA). Usually, the resource to be allocated is directly related to the interests of decision-making units (DMUs), thus the dynamic non-cooperative game is one of the representative behaviours in the allocation process. However, it is rarely considered in the previous DEA-based allocation studies, which may reduce the acceptability of the allocation plan. Therefore, this paper proposes a DEA-based resource allocation method considering the dynamic non-cooperative game behaviours of DMUs. The authors first deduce the efficient allocation set under the framework of variable return to scale (VRS) and build the allocation model subjecting to the allocation set. Then an iteration algorithm based on the concept of the non-cooperative game is provided for generating the optimal allocation plan. Several interesting characteristics of the algorithm are proved, including i) the algorithm is convergent, ii) the optimal allocation plan is a unique Nash equilibrium point, and iii) the optimal allocation plan is unique no matter which positive value the initial allocation takes. Some advantages of the allocation plan have been found. For example, the allocation plan is more balanced, has more incentives and less outliers, compared with other DEA-based allocation plans. Finally, the proposed method is applied to allocate the green credit among the 30 Chinese iron and steel enterprises, and the results highlight the applicability of the allocation method and solution approach. Therefore, the approach can provide decision makers with a useful resource allocation tool from the perspective of dynamic non-cooperative game.

     

Robust guaranteed cost filtering for uncertain timedelay systems with Markovian jumping parameters

1.INTRODUCTION Itisnowwellknownthatstochasticmodelinghas cometoplayanimportantroleinmanybranchesof scienceandindustry.Anareaofparticularinterest hasbeenMarkovjumpsystems[1～5].Ontheother hand,stateestimationplaysanimportantroleinsys temsandcontroltheory,signalprocessingandinforma tionfusion.Certainly,themostwidelyusedestimation methodisthewell knownKalmanfiltering[6,7].Acom monfeatureintheKalmanfilteringisthatanaccurate modelisavailable.Insomeapplications,however,when thesystemissubject…     

考虑下游ICU病床容量约束的鲁棒手术计划调度

手术计划调度是医疗资源配置的重要组成部分,也是复杂的组合优化问题.由于在手术计划调度过程中,存在手术时间、术后重症监护病房(intensive care unit,ICU)内住院时间、急诊病人的到达、病人取消等不确定因素.本文考虑手术后下游的ICU中病床资源的容量约束,基于不确定的手术时间和术后ICU住院时间,借助ellipsoid和box不确定集合刻画不确定性,提出一个手术计划调度两阶段鲁棒优化模型,得出易求解的鲁棒等价问题,并提出列生成启发式算法.算例结果表明,较之住院时间的不确定性,手术时间的不确定性对总成本和手术块的加班时间影响显著,而住院时间的不确定性对ICU内短缺病床数量有显著影响.管理者可选择恰当的手术时间和住院时间的不确定水平参数组合(Ω,Γ),综合权衡手术块的加班时间和ICU内病床的短缺数量,尽可能地最大化手术室、ICU病床资源的利用率.     

Robust L_1 filtering with pole constraint in a disk via parameter-dependent Lyapunov functions

1.INTRODUCTION Filteringisaveryimportantissueinsystemsdiagno sis,surveillanceandcontrol.Byfiltering,wereferto theproblemwhichamountstoextracttheinformation fromthemeasuredoutputtoprovideanestimateof thestate(oralinearcombinationofthestate).Clas sicalworksinthisdomainaretheonesofKalmanin thestochasticframe work[1].Inthecasewherethere existsuncertaintyinthemodelandinsufficientstatis ticalinformationaboutthenoiseinput,theKalman filteringschemeisnolongerapplicable.Therefore, validstrategi…     

最优下行链路功率分配策略和波束形成算法

针对下行链路的预编码问题,提出了一种基于二阶锥优化理论的求解方法。它将功率分配和波束形成的联合优化问题转变为一个单变量的凸优化问题,该凸规划问题实质上是一个二阶锥可表问题,因而可以利用内点法求取其全局最优解。更进一步,文章提出的算法还能对非精确的信道状态信息提供稳健性。仿真实验证明了算法的有效性。     

一类基于投入约束的资源配置DEA博弈模型

结合数据包络分析(DEA)与Nash讨价还价博弈模型研究有限资源的合理配置问题。首先证明,在投入约束条件下,基于传统DEA模型的资源配置方法将陷入困境。为此,需要考虑各决策单元(DMU)对于有限资源的竞合关系,引入Nash讨价还价博弈模型,并证明最优资源配置方案具有唯一性。最终通过算例说明了本方法的合理性与可行性,与其它DEA分摊方法相比还具有一定优势。     

一种基于剩余能量的无线网络资源分配算法研究

针对采用网状网模式组网的WMAN中,提出一种由移动站电量余量决定的分配优先级的系统带宽分配算法,按需分配网络资源并提高网络整体性能问题。通过提高重要节点收发数据时的时间集中度,降低重要节点收发数据时的功率消耗,较大程度降低了重复迭代计算的算法复杂度。该算法可以通过启发式分配等效可分配时间和发射功率来优化系统的资源分配,亦还可以通过改变效用函数的斜度参数满足不同的业务特性。     

基于分布式深度学习的边缘服务放置策略

针对移动边缘计算网络中不合理的服务放置和资源分配所导致的服务质量下降问题, 提出了一种基于分布式深度学习的边缘服务放置策略。首先, 以最小化所有用户服务请求时延与加权服务放置成本总和为优化目标, 将优化问题建模为混合整数非线性规划问题。其次, 在给定服务放置策略情况下, 利用凸优化理论求解出边云最优的计算资源分配方案。最后, 利用分布式深度学习解决了服务放置问题。理论证明及仿真结果表明, 所提策略能够有效降低用户服务请求时延和应用服务提供商的服务放置成本, 并且逐渐逼近全局最优的服务放置策略。     

基于不确定相关机会规划的机会阵方向图综合

围绕机会阵雷达(opportunistic array radar, OAR)阵列动态机会组阵的资源管理问题,以面向多任务为应用需求背景,针对机会布置在平台3D空间多个区域内的天线单元,提出了一种基于现代数学不确定性理论中的相关机会约束规划方法用于机会阵方向图综合。该方法建立在不确定性理论和模糊数学基础上,考虑OAR大量天线单元空间位置分布的不确定性和各单元激励(开/关)状态的不确定性,用模糊随机变量来刻画不确定环境中的模糊性和随机性,在天线资源受约束的不确定条件下,建立不确定规划模型来实现方向图综合。并设计将遗传算法和模糊随机模拟算法相结合的智能混合优化算法以获得模型的最优解。最后利用仿真实例验证了不确定规划模型和所设计算法的可行性和鲁棒性。