LTE混合组网中基于多目标粒子群的自规划

TDD-LTE与LTE-FDD混合组网对网络规划依赖较高。为实现LTE混合组网的基站选址自规划,使网络同时满足覆盖、能效比、负载、成本最优,提出了一种均衡多目标优化的混合组网基站选址规划模型。结合LTE混合组网理想回传的特点,重新构建多个目标优化函数,利用改进的离散多目标粒子群算法,从Pareto解集中模糊折衷选取较优解进行优化。仿真结果表明,提出的网络自规划方法能够综合提高网络性能同时减少成本,具有较好的应用价值。     

基于成本与网络负载最优的智能配电通信网络规划策略

针对配电网络单个通信终端节点数据量小、通信终端节点多的特点,在提高网络接入节点数量的基础上最大化网络的负载.在宏基站、微基站部署方式上,将宏基站与微基站同时部署,以提供给微基站更多的部署选择.在分析智能配电网络通信业务需求、现有网络规划的弊端的基础上,建立了多目标优化模型,并与单目标优化模型进行对比分析.最后,从规划方案图以及网络性能分析上证明了本文所提策略的优越性.     

基于改进遗传算法的电力光传输网规划方法

为保证建成的电力通信网络满足智能电网坚强可靠、经济高效的需求,提出一种兼顾网络建设成本和网络可靠性的电力光传输网线路规划方法,设计出基于站点成环率和电压等级的可靠性函数,建立了线路规划问题的多目标优化模型,在此基础上,用改进的遗传算法对问题进行求解,仿真结果表明,提出的规划方法能够提供有效的线路规划方案,具备较优的算法性能。     

含柔性负荷的配电网智能检修策略分析

阐述两种不同的灵活负载模型的对比,探讨主动式灵活负载均衡网络的优化、调度模型以及更优化的调度策略,包括升级优化、多目标规模、基于价格的放大规划模型和多个时间尺度。     

多区域多维覆盖联合优化卫星任务规划

针对电子侦察卫星如何对多目标潜在区域进行普察的任务规划问题,通过分析电子侦察卫星的应用场景、任务需求及执行约束,将待观测区域网格化、观测时间离散化以及对观测频率频段化处理,建立解决该问题的时空频三维覆盖优化模型。通过改进编码结构、初始化以及对约束处理等方法,将遗传算法用于该模型的求解。仿真实验表明,改进的算法可以适应多卫星长编码多约束优化问题求解,模型可以优化任务规划结果。     

无线资源利用率合理区间评估模型探讨

在分析影响无线资源利用率统计值的主要因素的基础上,创新地提出了无线资源利用率合理区间评估模型,并结合某地市CDMA网络相关话务数据,给出了城区、郊区和农村不同场景下的无线资源利用率合理范围,为无线网络运营商优化无线资源配置以及网络规划建设提供借鉴。     

一种新的动态多目标优化位置区域划分方法

文章基于移动通信网络系统中的位置区域划分问题,介绍了多目标进化算法,并提出了一种新的动态多目标优化位置区划分方法,从理论上分析该方法能有效避免传统算法中不能进行多目标的规划、不能进行全网优化等的不足。     

WCDMA无线网络规划的分析

目前，WCDMA无线网络的规划一般来说包括6个步骤：规划目标的定义、传播模型校正、名义小区规划、站址勘察、无线网络设计及初始网络优化。其中规划目标的定义是整个网络规划的前提，它包括覆盖目标、容量目标和质量目标3个方面。初始网络的覆盖和质量的要求虽然也比较复杂，但根据现有网络经验、     

WCDMA无线网络规划的分析

目前，WCDMA无线网络的规划一般来说包括6个步骤：规划目标的定义、传播模型校正、名义小区规划、站址勘察、无线网络设计及初始网络优化。其中规划目标的定义是整个网络规划的前提，它包括覆盖目标、容量目标和质量目标3个方面。初始网络的覆盖和质量的要求虽然也比较复杂，但     

融入偏好的武器装备体系多目标规划决策方法

武器装备体系规划是装备体系建设的重要环节,对装备论证工作具有重要指导意义.首先对武器装备体系规划问题进行了定性分析和公式化描述;然后提出基于超网络的体系建模方法,构建能力指标定量计算模型;接着采用NSGA-II算法范式,分析了决策者偏好表示方法,将决策者偏好融入优化过程,将原优化问题转化为以超体积、未知度、决策者满意度为目标的3目标优化问题,融入延展性测度对占优和进化策略进行重新设计;最后引用一则空基作战体系规划示例,验证了该算法的高效性和合理性,可为武器装备发展和顶层规划提供决策方法支撑.     

Resource allocation optimization algorithm based on double populations differential evolution in WSN

A resource allocation algorithm was proposed for improving the network performance through jointing channel allocation,power control and timeslot allocation in multi-radio multi-channel wireless sensor network.More specifically,the network was modeled as a multi-objective optimization problem where the energy efficient,resource allocation balanced,networks capacity maximized were considered under the link interference and link conflict constraints.Due to the problem was NP-Hardness,a simple centralized algorithm——differential evolution based on double populations was used to solve the constrained multi-objective optimization problem.The simulation results show that the proposed algorithm significantly improves the network capacity and energy efficiency and guarantees the resource allocation balancing while reducing link interference and avoiding link conflict.     

Adaptive routing and wavelength assignment method based on SDN

Routing and wavelength assignment is an important resource allocation method of all-optical network.Aiming at the problem of traditional method combined with the new architecture,an adaptive multi-objective routing and wavelength assignment method based on SDN was proposed,which could realize the allocation of link resources of all-optical network through self-regulation.Based on the SDN service function chain model,service scheduling time and link service quality were taken as the scheduling objective,routing and wavelength assignment problem was constructed as the 0-1 integer programming problem,meanwhile,binary hybrid topology particle swarm optimization algorithm was used to optimize the network resources for optimal scheduling.The simulation results show that the proposed method is superior to the traditional classical algorithms in the test of recovery time,blocking rate and resource utilization.     

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.     

一种基于MPLS网络的优化模型算法

M PLS已经成为NGN发展的重要技术,为了通过有效流量工程来达到提供Q oS和优化资源分配的目的,可以用混合整数数学方程(优化)技术来模拟路由问题,可以使构建、应用和维护网络成本最低,因此本文提出了一种基于M PLS网络链路计算的优化模型算法,使其应用于M PLS网络规划,根据容量和网络结构限制以及标签交换路径生存能力来安排路由,并结合两个网络实例得到证实。     

求解多目标资源分配问题的改进蚁群优化算法

多目标资源分配问题就是将有限资源分配到不同事件来获得预期目标.建立了多目标资源分配问题的数学模型,提出了一种有效求解该问题的改进蚁群优化算法：设计了一种多目标资源分配问题的可行方案构建机制,定义了蚁群优化算法中的信息素形式及其更新方式,提出了一种新的概率选择形式;通过以上改进有效地提高了蚁群优化方法的效率.为了验证此方法的有效性,将蚁群优化方法与混合遗传算法的实验结果进行了对比分析,证明此方法优于混合遗传算法.     

Cross-layer network planning for multi-radio multi-channel cognitive wireless networks

We propose a general network planning framework for multi-radio multi-channel wireless networks. Under this framework, data routing, resource allocation, and scheduling are jointly designed to maximize a network utility function. We first treat such a cross-layer design problem with fixed radio distributions across the nodes and formulate it as a large-scale convex optimization problem. A primal-dual method together with the column-generation technique is proposed to efficiently solve this problem. We then consider the radio allocation problem, i.e., the optimal placement of radios within the network to maximize the network utility function. This problem is formulated as a large- scale combinatorial optimization problem. We derive the necessary conditions that the optimal solution should satisfy, and then develop a sequential optimization scheme to solve this problem. Simulation studies are carried out to assess the performance of the proposed cross-layer network planning framework. It is seen that the proposed approach can significantly enhance the overall network performance.     

基于膜量子布谷鸟搜索的双通道网络频谱资源分配

无线双通道Ad Hoc网络中, 有效分配簇间码分频谱资源是提高资源利用效率的关键技术之一.综合考虑子簇码分频谱资源需求和分配公平性, 给出了簇间码分频谱资源分配数学模型, 并转换为以最大化码分频谱资源效益和分配公平性为多目标的受约束离散优化问题.结合膜结构、量子计算和布谷鸟搜索算法, 提出一种新的离散组合优化算法——膜量子布谷鸟搜索算法.该算法使用量子鸟窝表征问题潜在解, 利用布谷鸟寻窝产卵的演化方法在基础膜中寻求单目标最优解, 通过膜间信息共享和非支配解等级排序求出具有多目标最优解的表层膜Pareto前端解集.仿真结果证明, 与经典优化算法相比, 该算法不仅能够同时求解单目标和多目标最优解, 而且具有更优的收敛性能, 能更好地实现码分频谱资源效益最优化.     

基于异步优势演员-评论家学习的服务功能链资源分配算法

考虑网络全局信息难以获悉的实际情况,针对接入网切片场景下用户终端(UE)的移动性和数据包到达的动态性导致的资源分配优化问题,该文提出了一种基于异步优势演员-评论家(A3C)学习的服务功能链(SFC)资源分配算法。首先,该算法建立基于区块链的资源管理机制,通过区块链技术实现可信地共享并更新网络全局信息,监督并记录SFC资源分配过程。然后,建立UE移动和数据包到达时变情况下的无线资源、计算资源和带宽资源联合分配的时延最小化模型,并进一步将其转化为马尔科夫决策过程(MDP)。最后,在所建立的MDP中采用A3C学习方法,实现资源分配策略的求解。仿真结果表明,该算法能够更加合理高效地利用资源,优化系统时延并保证UE需求。     

基于可靠性的5G网络切片在线映射算法

为了满足业务多样性对5G网络切片带来差异化需求的同时保证切片的可靠性,实现网络资源的优化配置。该文针对5G网络切片的动态映射、轻量级可靠映射问题,提出对计算资源、链路资源和RRU频谱资源联合分配方案。首先,该方案建立面向可靠性约束的多目标资源分配模型,引入李雅普诺夫优化模型,在保证队列稳定同时优化资源分配。其次,提出了基于队列稳定性的虚拟节点映射算法和基于可靠性的虚拟链路映射算法。最后,将时间离散为一系列连续的时间窗,利用时间窗动态处理到达的网络切片请求,实现在线的网络切片映射算法。仿真结果表明,该算法提高了资源利用率,并且保证网络可靠性。     

A low-cost cellular mobile communication system: a hierarchicaloptimization network resource planning approach

This paper deals with the optimization of the design and radio network resource planning for cellular mobile communication systems. The key element to be considered for mobile network planning is cost. A hierarchical optimization planning method (HOP) is utilized since there are so many factors like system performance, terrain features, base station parameters, and cost factors which are involved in the large-scale system design. We present a three-level optimization approach for designing a cellular mobile system. It determines the cell number, cell site allocation, and the specific base station parameters in order to minimize the total system cost and to comply with the required system performance. The problem is formulated as a large combinatorial optimization model which can determine the optimal number of cells and select the best base stations' locations. The simulated annealing approach is developed to solve the hard combinatorial problem. Simulation results demonstrate the feasibility and effectiveness of the HOP approach for radio network planning