考虑负载均衡的系统保护通信专网路由规划方法

针对系统保护通信专网局部节点与链路业务负载过重的问题,提出一种考虑负载均衡的系统保护通信专网路由规划方法。首先,阐述了系统保护通信专网的概念,分析了局部节点和链路业务负载过重的问题;然后,构建了综合考虑业务特性和备份路径的负载均衡路由规划优化模型,实现了模型的负载均衡路由规划求解;最后,以某省系统保护通信专网光传送网(OTN)拓扑为仿真网络,采用PSCPlanner电力通信网规划平台与K条最短路径(KSP)算法进行了对比实验。仿真结果表明:该方法均衡了链路容量,降低了业务请求拒绝率,有效地分配了网络资源。     

WCDMA下行链路软切换增益分析

在WCDMA无线网络中，由于新业务的非对称性，下行链路更有可能成为系统容量的瓶颈。研究了在理想功率控制下路径损耗因子、阴影标准方差等不同无线参数对下行链路软切换容量增益的影响，为无线网络规划提供了一定的依据。     

关于传输虚拟化中数据分组乱序问题的研究

对传输虚拟化相关问题及传输虚拟化中多路径并发传输的数据分组乱序问题进行了介绍,从静态的路径选择机制以及动态的数据分组调度模型方面分析了造成接收端数据分组乱序的主要原因,提出了基于路径往返时间的路径选择机制和基于权重轮询调度算法、时延均衡技术的数据分组调度模型.理论分析和仿真实验的结果表明,所提方案能够明显地缓解接收端数据分组的乱序问题,有效地降低了接收端缓存区容量的需求.     

无线传感器网络中联合路由优化的高能效链路调度

链路调度技术是提高无线传感器网络数据传输可靠性的重要途径.传统的链路调度算法主要关注如何寻找无干扰传输条件下的最小调度帧,而忽视路径选择对链路调度的影响,会导致部分链路的干扰集过大,降低了时隙复用性.针对这一难题,系统分析了链路调度与路径选择及网络能耗的关系,提出联合路径优化的高能效链路调度模型.为加快求解速度,首先通过变量删减及模型转化,提出基于整数规划的最优路由树构建算法;进一步,根据整数规划求解的路由树与链路需求,提出基于最大干扰度优先的启发式链路调度算法.大量仿真实验验证了本文算法的有效性.     

3G无线网络覆盖和容量的规划设计

随着我国3G系统建设的全面启动,3G网络规划设计已成为研究的热点之一。为了实现建设目标,在进行3G无线网络规划设计时,必须同时考虑覆盖、容量及不同服务质量的要求。文章对无线网络覆盖和容量的规划设计进行了论述,分析了覆盖和容量的关系,讨论了覆盖规划、容量规划等几个重要问题。特别对链路预算、传播模型、容量估算和极限容量等进行了阐述,给出了上行极限容量分析及上行链路容量规划的举例。     

一种适用于星地网络的流量负载均衡算法

在星地网络的背景下,为了降低由于地面网络链路负载过高导致的传播时延,提出了一种适用于星地网络的负载均衡算法。算法基于网络的链路容量设置一个链路资源利用率作为阈值,当发现路径出现过载的时候分成两步进行路径的预规划：第一步,运用蚁群算法为链路过载的数据流选择其他的路径,以满足地面网内部的负载均衡;第二步,如果仍出现链路超阈值的情况,通过卸载延迟容忍数据流来执行地面与卫星网络之间的负载均衡。仿真结果表明,设定自适应的过载阈值可以有效降低链路的拥塞,与现有算法相比该算法不仅可以更加均匀地分配地面网络中的流量,而且借助卫星网络使地面网络的平均时延降低了18.3%,提高了网络的服务质量。     

基于TD-HSUPA新增信道链路预算

本文简要概述了TD-HSUPA新增物理信道以及链路预算的一般应用模型,详述了链路预算基本参数取值和传播模型的选取,并对TD-HSUPA新增物理信道进行初步链路预算并估算出其小区覆盖半径,来解决容量和覆盖规划的平衡问题。     

基于TD-HSUPA新增信道链路预算

本文简要概述了TD-HSUPA新增物理信道以及链路预算的一般应用模型,详述了链路预算基本参数取值和传播模型的选取,并对TD-HSUPA新增物理信道进行初步链路预算并估算出其小区覆盖半径,来解决容量和覆盖规划的平衡问题.     

基于DQN的UUV辅助水下无线光通信轨迹规划系统

无人水下航行器（unmanned underwater vehicle,UUV）作为重要潜基通信平台可以辅助水下无线光通信（underwater wireless optical communication,UWOC）。然而，在实际应用中，水体波动特性、不同水质环境、多用户接入等给UUV辅助UWOC系统带来很大挑战，因而适当的路径规划策略可以应对上述挑战并最大限度地提升系统整体和每一个用户的性能。将深度强化学习（deep reinforcement learning,DRL）用于无人载具路径规划中，提出了一种UUV辅助UWOC系统的轨迹规划方案。通过DRL中深度Q网络（deep Q-network,DQN）方法让UUV自动决策航行方向，从而提升系统和用户的链路通信容量。同时，研究了不同水质对容量提升效果的影响。仿真实验表明，DQN输出策略可以在一定程度上提升系统整体和各个用户的链路通信容量，并且UUV在清澈海水中的容量提升效果优于纯净海水但低于沿岸水。     

基于强化学习的定向无线通信网络抗干扰资源调度算法

为了在无线网络中进行高效的链路资源调度、减小网络干扰、提高网络容量,提出了一种利用回溯天线并考虑干扰环境的链路资源分布式智能调度算法.首先,结合通信的路径损耗模型设计卷积核,对节点密度矩阵进行卷积来衡量干扰链路强度,从而避免对所有干扰链路进行信道估计产生巨大的计算代价;然后,结合强化学习的思想设计了与通信环境交互的链路调度学习模型,每个链路利用神经网络进行独立的训练,将训练所得的决策结果反馈到环境中进行状态更新,模型在不断更新的环境中迭代来学习最优的调度策略.该方法能分布式的运行,可有效衡量无线网络中的链路干扰强度,结合衡量结果进行高效的链路资源分布式调度,从而最大化网络容量.仿真结果验证了该调度算法无论是在算法迭代收敛还是网络容量性能上都能很好地逼近全局的调度算法,达到全局算法最优结果的92%~100%.     

On the planning of the S-PVC overlay in ATM networks

We tackle the planning problem of the soft-permanent virtual connections (S-PVCs) in asynchronous transfer mode (ATM) networks. The private network-to-network interface (PNNI) routing standard is used to route these connections. The planning problem consists of determining the PNNI link metrics and the capacity to reserve on each ATM link that ensure the successful rerouting of the S-PVCs for all failure scenarios of interest to the network planner. A mathematical programming model is proposed and a tabu search heuristic in order to find "good" solutions. Finally numerical results are presented and analyzed     

Design of capacitated survivable networks with a single Facility

In this paper we focus on the single-facility capacitated survivable network design problem. We optimize simultaneously the network topology and the link dimensioning in order to route all traffic commodities according to survivability requirements. The latter are actually expressed in terms of the spare capacity required to address link failures in the context of different rerouting strategies. We present a mixed-integer linear programming model solved by combining several approaches. To tackle the high dimensionality and to separate the continuous and integer variables, we use Benders' decomposition and a cutting-plane approach. Going beyond the proposed method itself, we examine and compare two well-known restoration techniques: local and end-to-end reroutings. Numerous computational results for realistic network instances provide a comparison of these rerouting mechanisms in terms of installed capacities, network density as well as overall costs and CPU time.     

移动Ad hoc网络中基于自适应策略的稳定链路选择算法

该文提出了一种基于自适应策略的稳定链路选择算法,定义了稳定邻居度量和本地运动度量两种稳定测度。在此基础上,通过引入模糊隶属度函数得出稳定测度的重要性权值,利用重要性加权的方法即可根据网络状况自适应地得到链路的稳定概率,从而为选择最稳定链路及路由提供依据。仿真结果表明该算法能很好地适应当前的网络状况,明显优于基于LF因子和基于剩余生命时间的链路选择算法,稳定性能显著提高。     

Joint optimization of data network design and facility selection

The authors describe a data network design model based on a mixed integer/linear programming (MILP) formulation that does not, as do most other approaches, separate link capacity and facility selection from routing and topological design; it fully integrates these processes to capture the important couplings that exist between them. The performance constraints are incorporated into the model in such a way that they are linear, but lead to the same grade of service for a balanced network as nonlinear average network delay constraints. It is shown that this formulation leads to a natural decomposition of the optimal design problem into two subproblems solvable sequentially. In the absence of capacity allocation constraints, the capacity and flow assignment problem is solved optimally and efficiently as part of the overall design process. Moreover, the model leads directly to the solution of multifacility design problems. A fast link reduction algorithm that efficiently designs single or multifacility networks and yields robust local extrema is presented. This algorithm is based on a special-purpose monotonic greedy drop heuristic procedure. An important application of this model is the design of multifacility networks     

Tabu search for dynamic routing communications network design

This paper presents a tabu search approach for optimizing the link capacities in a dynamic routing telecommunications network. The traffic between any two nodes in the network is routed over a one‐link direct path or, if no direct capacity is available, over a two‐link alternate path. The alternate routing paths can be changed dynamically from hour to hour as the traffic between pairs of nodes may vary with the time of day. The problem is to determine the optimal capacity level for each link in the network to minimize cost while satisfying the grade‐of‐service constraints. Although the problem can be formulated as a nonlinear integer programming problem, no efficient solution procedures are available. In this paper, we develop a two‐level tabu search heuristic for solving the problem that utilizes probabilistic move selection and coordinated solution recovery strategies. The macro level of the algorithm iteratively determines an hour for possible improvement and then the micro level seeks to optimize the routing paths for that hour. Our computational experience with both real and simulated problems indicates that significant savings can be obtained by this approach over the conventional network designs. This revised version was published online in June 2006 with corrections to the Cover Date.     

Fuzzy generalized network approach for solving an optimization model for routing in B‐ISDN

Routing algorithms are required to guarantee the various quality of service (QoS) characteristics requested by the wide range of applications supported by Broadband Integrated Services Digital Networks (B‐ISDN). In this paper the routing problem is formulated as a fuzzy multiobjective optimization model. The fuzzy approach allows for the inclusion and evaluation of several criteria simultaneously. The proposed model takes into consideration the balancing of the load in the network to avoid link saturation and hence the possibility of congestion. A hybrid approach that combines the generalized network concept with the technique of fuzzy programming is recommended to solve the model. The efficiency and applicability of the model is tested under different load conditions by studying several measures of performance. This revised version was published online in June 2006 with corrections to the Cover Date.     

Restoration strategies and spare capacity requirements inself-healing ATM networks

This paper studies the capacity and flow assignment problem arising in the design of self-healing asynchronous transfer mode (ATM) networks using the virtual path concept. The problem is formulated here as a linear programming problem which is solved using standard methods. The objective is to minimize the spare capacity cost for the given restoration requirement. The spare cost depends on the restoration strategies used in the network. We compare several restoration strategies quantitatively in terms of spare cost, notably: global versus failure-oriented reconfiguration, path versus link restoration, and state-dependent versus state-independent restoration. The advantages and disadvantages of various restoration strategies are also highlighted. Such comparisons provide useful guidance for real network design. Further, a new heuristic algorithm based on the minimum cost route concept is developed for the design of large self-healing ATM networks using path restoration. Numerical results illustrate that the heuristic algorithm is efficient and gives near-optimal solutions for the spare capacity allocation and flow assignment for tested examples     

中继协同D2D通信的能效优化

针对D2D中继通信中D2D设备电池容量受限问题,本文提出了一种逐步优化功率控制和中继选择从而最大化系统能量效率的方法。首先,在满足蜂窝链路最小数据速率的条件下,将功率控制问题建模为一个非线性规划问题,并求解出D2D发射机的最优发射功率;然后,分别利用发送节点与接收节点的邻域为半径建立节点均匀分布的球状模型;其次,基于路径损耗相似性对节点进行k-means迭代聚类,并得到路径损耗最小节点的质心;最后,将得到的质心作为中继节点进行D2D中继通信。仿真结果表明,在保证高质量通信的条件下,本文提出的优化方法能够最大化系统的能量效率。      

Immune Algorithm for Solving the Optimization Problems of Computer Communication Networks

The basic problem in optimizing communication networks is to assign a proper circuit for each origindestination pair in networks so as to minimize the average network delay, and the network optimal route selection model is a multi-constrained 0-1 nonlinear programming problem. In this paper, a new stochastic optimization algorithm, Immune Algorithm, is applied to solve the optimization problem in communication networks. And the backbone network vBNS is chosen to illustrate the technique of evaluating delay in a virtual network. At last, IA is compared with the optimization method in communication networks based on Genetic Algorithm, and the result shows that IA is better than GA in global optimum finding.     

Practical cross‐layer routing and channel assignment in cognitive radio ad hoc networks

In the heterogeneous and unreliable channel environment of cognitive radio ad hoc networks (CRAHNs), a multipath route with channel assigned is preferable in both throughput and reliability. The cross‐layer multipath routing and channel assignment in CRAHNs is becoming a challenging issue. In this paper, this problem is characterized, formulated, and shown to be in the form of mixed integer programming. For this Non‐deterministic Polynomial‐time (NP)‐hard problem, the deficiency of the widely used linearization and sequential fixing algorithm is first analyzed. The main contribution of this paper is the development of a new backtracking algorithm with feasibility checking to search optimal solutions and a heuristic algorithm with high feasible solution‐obtained probability (HHFOP) for distributed application in CRAHNs. Through feasibility checking and solution bounds validating, backtracking algorithm with feasibility checking cuts off unnecessary searching space in early stage without loss of optimal solutions, making it much more efficient than brute searching. For practical application in CRAHNs with polynomial complexity, HHFOP first computes the maximal‐supported throughput through link‐channel assignment and link‐capacity coordination for each candidate path. Then the paths are combined, and the route throughput is optimized. Extensive simulation results demonstrate that HHFOP can achieve a high feasible solution‐obtained probability with little throughput degradation compared with linearization and sequential fixing algorithm, indicating its practicability for distributed applications in CRAHNs. Copyright © 2013 John Wiley & Sons, Ltd.