Ad Hoc 网络节能型功率控制与拥塞控制的跨层优化

有限的节点能量和通信带宽,是Ad Hoc网络的两个重要的特点.节点能量是影响网络容量的关键因素,也是制约网络寿命的决定因素;而有限的通信带宽使得网络更容易产生拥塞.因此,节能型的功率控制与拥塞控制联合优化在Ad Hoc网络中显得尤为重要.首先,设计了节能型的网络效用最大化问题,即在目标函数中引入能量消耗成本函数,从网络效用和网络寿命两个方面来综合优化网络性能.其次,运用对偶分解与梯度投影方法,提出了相应的节能型功率控制与拥塞控制联合优化算法.另外,分析和证明了所提算法的收敛性.最后,详细的仿真结果表明了所提算法的有效性:在保持网络吞吐量基本不变的同时,可以有效地减少节点的能量消耗,从而延长网络寿命.     

Rate-lifetime tradeoff for reliable communication in wireless sensor networks

The network lifetime and application performance are two fundamental, yet conflicting, design objectives in wireless sensor networks. There is an intrinsic tradeoff between network lifetime maximization and application performance maximization, the latter being often correlated to the rate at which the application can send its data reliably in sensor networks. In this paper we study this tradeoff by investigating the interactions between the network lifetime maximization problem and the rate allocation problem with a reliable data delivery requirement. Severe bias on the allocated rates of some sensor nodes may exist if only the total throughput of the sensor network is maximized, hence we enforce fairness on source rates of sensor nodes by invoking the network utility maximization (NUM) framework. To guarantee reliable communication, we adopt the hop-by-hop retransmission scheme. We formulate the network lifetime maximization and fair rate allocation both as constrained maximization problems. We characterize the tradeoff between them, give the optimality condition, and derive a partially distributed algorithm to solve the problem. Furthermore, we propose an approximation of the tradeoff problem using NUM framework, and derive a fully distributed algorithm to solve the problem.     

基于机会约束规划的无线Mesh网跨层优化算法

以无线Mesh网的联合拥塞控制与功率控制为优化目标,针对网络中的不可控数据流与无线传播环境的时变随机性两类随机性因素,结合随机网络效用最大化理论,建立了无线Mesh网的跨层联合优化模型。将无线Mesh网络中的不可控数据流和时变无线传播的干扰建模为随机变量,采用机会约束规划方法进行分析,最后利用遗传算法求解该随机优化问题,并进行了仿真验证。仿真结果反映了网络速率、节点发射功率与链路置信水平三者之间的定量制约关系。     

基于遗传算法的计算机通信网的拓扑优化设计

1 引言设计计算机通信网的一个基本要求是网络全局有效性,即连通概率。从网络角度,连通概率指的是网络至少简单连通。其除依赖于各计算机系统和通信能力外,主要依赖于通信链路的拓扑设计。对一个给定计算机通信网的最大全局可靠性的网络拓扑优化设计,人们已提出许多启发式算法,但这些算法并未给出精确解。本文采用遗传算法进行设计,成功地解决了这类问题。     

一种无线传感器网络的优化速率控制方法

拥塞控制是无线传感器网络中的一个关键性问题。从解决拥塞问题出发,提出了一个基于优化速率的拥塞控制算法ORCC。算法构建了一个分布式的分簇网络结构,利用缓冲的占用情况进行拥塞检测,并使用基于优化理论的速率调节策略来保证网络吞吐量的稳定,从而通过求解出的最优解来实现簇内节点效用的最大化。仿真实验表明,ORCC算法不仅能有效缓解网络拥塞,降低平均延迟,还具有较好的网络传输公平性。     

NOMA系统异构网络中联合用户关联和功率控制协同优化

针对非正交多址接入(Non-Orthogonal Multiple Access,NOMA)系统的两层异构网络,提出了基于效用函数最大化模型的用户关联与功率控制协同优化问题.在该问题中将系统总能效作为效用函数,在一定的用户服务质量要求(QoS)和最大功率限制约束下,提出一种联合用户关联和功率控制的算法.该算法首先将原问...     

传输合同约束下编码分组网络的效用最大化研究

研究在传输合同约束条件下当编码子图给定时编码分组网络的效用最大化问题。基于提出的网络效用最大化模型,通过对偶分解理论,提出了分布式的次梯度投影算法,证明了算法收敛的充分条件,最后通过仿真验证了算法的正确性。     

Stochastic utility-based flow control algorithm for services with time-varying rate requirements

In this paper, we study a utility based flow control problem for a communication network. In most previous works on utility based flow control, the utility function of each user, which represents its satisfaction to the allocated data rate, is assumed to be fixed. This implies that the degree of the rate requirement of each user is assumed to be fixed over the entire duration of its session. However, in communication networks, many services are variable rate services, i.e., the degree of their rate requirement varies over time, which cannot be modeled with traditional static utility functions. To resolve this issue and appropriately model services with variable rate requirements, we propose a stochastic utility function that varies stochastically according to the variation of the degree of the rate requirement of a service. We formulate a flow control problem as a stochastic optimization problem with stochastic utility functions that aims at maximizing the average network utility while satisfying the constraint on link capacity and QoS requirement. By solving the stochastic optimization problem, we develop a distributed flow control algorithm that converges to the optimal rate allocation.     

Utility-based scheduling in wireless multi-hop networks over non-deterministic fading channels

In this paper, an innovative scheduling scheme is proposed for interference-limited wireless multi-hop networks with non-deterministic fading channels. The scheduling problem is considered as a network utility maximization (NUM) problem subject to link rate constraints. By jointly taking into account of the link scheduling and the statistical variations of signal and interference power, the convex sets for the NUM are derived. Two types of non-deterministic fading channels (i.e., Rayleigh fading channel and Ricean fading channel) are characterized into our NUM models as examples. To solve the convex optimization problem, the subgradient projection method based on dual decomposition is employed. Then, a heuristic algorithm is designed for the TDM mode wireless multi-hop networks by minimizing the discrepancy between the expected network cost and the optimal one in each timeslot. At last, the source–destination session rate and network utility are evaluated in a dedicated wireless multi-hop network scenario. The numerical results demonstrate that the session rates convergence and the network utility is improved by our proposed scheme.     

Jointly optimal congestion control, channel allocation and power control in multi-channel wireless multihop networks

In this paper, to increase end-to-end throughput and energy efficiency of the multi-channel wireless multihop networks, a framework of jointly optimize congestion control in the transport layer, channel allocation in the data link layer and power control in the physical layer is proposed. It models the network by a generalized network utility maximization (NUM) problem with elastic link data rate constraints. Through binary linearization and log-transformation, and after relaxing the binary constraints on channel allocation matrix, the NUM problem becomes a convex optimization problem, which can be solved by the gateway centralized through branch and bound algorithm with exponential time complexity. Then, a partially distributed near-optimal jointly congestion control, channel allocation and power control (DCCCAPC) algorithm based on Lagrangian dual decomposition technique is proposed. Performance is assessed through simulations in terms of network utility, energy efficiency and fairness index. Convergence of both centralized and distributed algorithms is proved through theoretic analysis and simulations. As the available network resources increase, the performance gain on network utility increases.     

无线充电体域网基于边际效用的网络资源分配方法

射频能量采集技术可以从根本上解决电池容量对无线体域网生存期的限制,为了提高网络资源分配的效率以及公平性,提出一种基于边际效用理论的网络资源分配方法。首先,设计传感器节点的效用函数,将节点所能获得的吞吐量映射成QoS满意的等级;然后,以最大化网络中全部传感器节点整体效用为目标,将多高效、公平的网络资源分配问题构建成效用最大化问题;最后,通过对偶分解方法求得该问题的最优解。仿真结果表明,与总吞吐量最大化和sigmoid效用最大化方法相比,所提出的方法在获得较高系统整体吞吐量的同时,确保了传感器节点个体获得吞吐量的公平性。     

基于粒子群的CDMA功率和速率联合控制算法

提出了基于效用函数的CDMA网络下行链路的功率和速率联合控制最优化算法.在这类算法中,效用函数为非凸函数,经典的最优化理论很难解决这类问题.将粒子群优化方法应用于算法的非凸性设计,并通过仿真算例证明了该算法能有效解决非凸优化问题,且可保证系统的公平性.     

基于粒子群的TCP非凸优化速率控制算法

为了有效地解决网络中拥塞问题,针对实际网络中存在非弹性流的情况,考虑了网络中非凸优化速率控制问题。基于最大化用户效用函数框架,去掉了以往研究中对效用函数的严格假设,利用粒子群方法设计了分布式速率控制算法。算法中链路从网络获知拥塞链路的条数,用户根据对应的效用函数和拥塞反馈信息调整自身速率。仿真结果表明,算法可以很快地收敛到最优速率。     

Distributed rate control,routing, and energy management in dynamic rechargeable sensor networks

This paper studies the network utility maximization (NUM) problem in dynamic-routing rechargeable sensor networks (RSNs), where rate control, routing, and energy management need to be jointly optimized. This problem is very challenging since the flow constraint is spatially coupled and the energy constraint is spatiotemporally coupled (energy causality). Existing works either do not fully consider the two coupled constraints together, or heuristically remove the temporally-coupled part, both of which are not practical, and may degrade network performance. In this paper, we attempt to jointly optimize rate control, routing, and energy management by carefully tackling the flow and energy constraints. To this end, we first decouple the original problem equivalently into separable subproblems by means of dual decomposition. Then, we propose a distributed algorithm, which can converge to the globally optimal solution. Numerical results based on real solar data are presented to evaluate the optimality and scalability of the proposed algorithm.     

基于效用的超宽带系统带宽分配

把超宽带系统的带宽优化调度表示为一个效用最大化的问题。对于系统的带宽分配,效用函数是服务质量的有效度量,它反映了用户对所分配的资源的满意程度。针对超宽带无线网络带宽分配中链路和用户的集中式算法的复杂性,用分布式方案解决这种问题,以自适应变化的无线网络环境。对系统带宽进行基于效用的分配,满足超宽带系统高速率传输的需要。     

A joint maximum likelihood algorithm for simultaneous track correlation and sensorbias estimation

In this study a joint maximum likelihood （JML） algorithm was developed to solve problems re- garding interdependent and contradictory relationships between track correlation and sensor bias estimation in multi-sensor information fusion systems. First, the relationships between track correlation and sensor bias estimation of a multi-sensor system were analyzed. Then, based on these relationships, a JML function of the track correlation and sensor bias estimation was developed, while an iterative two-step optimization procedure was adopted to solve the JML function. In addition, transformation of sensor bias from Cartesian coordinates to polar coordinates and a complete design of track quality and ambiguity processing were provided. Finally, several Monte Carlo simulations were built to test the effect of target density and different sensor bias in the JML algorithm. Simulation results showed that the JML algorithm presented in this paper had a higher correct correlation rate and more accurate sensor bias estimation than traditional methods, demonstrating that the JML algorithm had good performance.     

Nonconvex resource control and lifetime optimization in wireless video sensor networks based on chaotic particle swarm optimization

Wireless video sensor networks (WVSNs) have attracted a lot of interest because of the enhancements that they offer to existing wireless sensor networks applications and their numerous potential in other research areas. However, the introduction of video raises new challenges. The transmission of video and imaging data requires both energy efficiency and quality of service (QoS) assurance in order to ensure the efficient use of sensor resources as well as the integrity of the collected information. To this end, this paper proposes a joint power, rate and lifetime management algorithm in WVSNs based on the network utility maximization framework. The optimization problem is always nonconcave, which makes the problem difficult to solve. This paper makes progress in solving this type of optimization problems using particle swarm optimization (PSO). Based on the movement and intelligence of swarms, PSO is a new evolution algorithm to look for the most fertile feeding location. It can solve discontinuous, nonconvex and nonlinear problems efficiently. First, since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the paper introduces chaos mapping into PSO with adaptive inertia weight factor to avoid the disadvantage of original PSO of easily getting to the local optimal solution in the later evolution period and keep the rapid convergence performance. Second, based on the distribution characteristics of the actual network, we decompose the resource control problem into a number of sub-problems using the hierarchical thought, where each user corresponds to a subsystem which is solved using the proposed CPSO3 method. Through the cooperative coevolution theory, these sub-optimization problems interact with each other to obtain the optimum of the system. Numerical examples show that our algorithm can guarantee fast convergence and fairness within a few iterations. Besides, it is demonstrated that our algorithm can solve the nonconvex optimization problems very efficiently.     

Multi-path utility maximization and multi-path TCP design

The canonical multi-path network utility maximization (NUM) model which is extended directly from the single-path NUM has been studied widely in the literature. Most of the previous approaches do not specify the case of subflows on paths with different characteristics. Moreover, the transport protocol derived from the canonical multi-path NUM exhibits flappiness in the subflows because of the non-strictly convexity of the optimization problem.     

无线传感网络中拥塞控制与路由的跨层设计:分布式牛顿法

无线传感网络应用广泛, 其性能与路由选择和拥塞控制密切相关. 致力于拥塞控制与多径路由的跨层优化, 以实现在链路容量受限和节点能量受限情况下的无线传感网络效用最大化. 针对对偶次梯度算法具有收敛速度慢与信息交互量大等缺陷, 设计了具有二阶收敛性能的分布式牛顿算法来实现网络效用最大化. 通过矩阵分裂技术, 实现了只需单跳信息交互的牛顿对偶方向的分布式求解方法. 仿真结果表明, 分布式牛顿算法的收敛性能显著优于对偶次梯度算法.     

Nonconvex dynamic spectrum allocation for cognitive radio networks via particle swarm optimization and simulated annealing

Dynamic spectrum access is a promising technique designed to meet the challenge of rapidly growing demands for broadband access in cognitive radio networks. By utilizing the allocated spectrum, cognitive radio devices can provide high throughput and low latency communications. This paper introduces an efficient dynamic spectrum allocation algorithm in cognitive radio networks based on the network utility maximization framework. The objective function in this optimization problem is always nonconvex, which makes the problem difficult to solve. Prior works on network resource optimization always transformed the nonconvex optimization problem into a convex one under some strict assumptions, which do not meet the actual networks. We solve the nonconvex optimization problem directly using an improved particle swarm optimization (PSO) method. Simulated annealing (SA), combined with PSO to form the PSOSA algorithm, overcomes the inherent defects and disadvantages of these two individual components. Simulations show that the proposed solution achieves significant throughput compared with existing approaches, and it is efficient in solving the nonconvex optimization problem.