蜂窝系统功率控制算法

无线蜂窝系统中的功率控制是保证服务质量、减少传输功率的重要方法.在假设系统具有随机噪声和用户信干比(S IR)具有估计误差的情形下,提出了一个新的分散迭代功率控制算法.此算法中每一用户根据信干比估计调整功率标准,不需要任何信道增益和其它用户的状态信息.因此,提出的算法具有鲁棒性.利用随机逼近方法证明了算法的收敛性.仿真结果表明本文算法是有效的.     

Asymptotic optimality for consensus-type stochastic approximation algorithms using iterate averaging

This paper introduces a post-iteration averaging algorithm to achieve asymptotic optimality in convergence rates of stochastic approximation algorithms for consensus control with structural constraints. The algorithm involves two stages. The first stage is a coarse approximation obtained using a sequence of large stepsizes. Then, the second stage provides a refinement by averaging the iterates from the first stage. We show that the new algorithm is asymptotically efficient and gives the optimal convergence rates in the sense of the best scaling factor and ’smallest’ possible asymptotic variance.     

无线体域网低能耗功率控制与分布式调度算法

传输能耗在无线体域网的能耗中占比最大,降低传输能耗的核心是解决2个问题:传输时隙到来时选择哪个节点传输和所选节点以何种功率传输.针对前者现有调度算法忽视网络公平性要求的局限性,提出了一种包含公平性指数的分布式调度算法;针对后者当前功率控制算法采用固定函数模型的局限性,设计了一种基于比例—积分—微分的单输出神经网络(SPIDNN)自适应网络功率机制动态调整无线体域网中传感器节点的发射功率控制方法.仿真实验表明:相对其他方案,方案在保证网络公平性的前提下,降低了能量消耗.     

Distributed and recursive blind channel identification to sensor networks

In this paper, the distributed and recursive blind channel identification algorithms are proposed for single-input multi-output (SIMO) systems of sensor networks (both time-invariant and time-varying networks). At any time, each agent updates its estimate using the local observation and the information derived from its neighboring agents. The algorithms are based on the truncated stochastic approximation and their convergence is proved. A simulation example is presented and the computation results are shown to be consistent with theoretical analysis.     

Continuous output-feedback stabilization for a class of stochastic high-order nonlinear systems

This paper is concerned with the global stabilization via output-feedback for a class of high-order stochastic nonlinear systems with unmeasurable states dependent growth and uncertain control coefficients. Indeed, there have been abundant deterministic results which recently inspired the intense investigation for their stochastic analogous. However, because of the possibility of non-unique solutions to the systems, there lack basic concepts and theorems for the problem under investigation. First of all, two stochastic stability concepts are generalized to allow the stochastic systems with more than one solution, and a key theorem is given to provide the sufficient conditions for the stochastic stabilities in a weaker sense. Then, by introducing the suitable reduced order observer and appropriate control Lyapunov functions, and by using the method of adding a power integrator, a continuous (nonsmooth) output-feedback controller is successfully designed, which guarantees that the closed-loop system is globally asymptotically stable in probability.     

Access point selection in heterogeneous wireless networks using belief propagation

The next generation wireless network will be composed by various heterogenous wireless access networks,such as cellular network,worldwide interoperability for microwave access(WiMAX),wireless local area network(WLAN),etc.Different access networks cooperatively provide high-bandwidth connectivity with bandwidth guarantees.This paper proposes a utility-based access point selection scheme,which selects an accessible point for each user,such that the bandwidth requirement of each user is satisfied,and also the defined utility function is maximized.Due to the NP-complete nature of the problem,the existing proposals apply the greedy method to find a solution.We find that belief propagation is an efficient tool to solve this problem,and thus,we derive the same optimization objective in a new way,and then draw a factor graph representation which describes our combinatorial optimization problem.Afterwards,we develop the belief propagation algorithm,and show that our algorithm converges.Finally,we conduct numerical experiments to evaluate the convergency and accuracy of the belief propagation in load balancing problem.     

虚拟控制增益函数未知的随机非线性系统模糊自适应控制

The problem of track control is studied for a class of strict-feedback stochastic nonlinear systems in which unknown virtual control gain function is the main feature. First, the so-called stochastic LaSalle theory is extended to some extent, and accordingly, the results of global ultimate boundedness for stochastic nonlinear systems are developed. Next, a new design scheme of fuzzy adaptive control is proposed. The advantage of it is that it does not require priori knowledge of virtual control gain function sign, which is usually demanded in many designs. At the same time, the track performance of closed-loop systems is improved by adaptive modifying the estimated error upper bound. By theoretical analysis, the signals of closed-loop systems are globally ultimately bounded in probability and the track error converges to a small residual set around the origin in 4th-power expectation.     

D2D通信中联合资源分配与功率控制算法

终端直通(D2D)技术引入移动蜂窝网络虽然能够提高蜂窝系统性能,但却带来了很大的干扰和能量消耗.为了降低干扰,提升频谱效率,同时又兼顾能量效率,提出了一种联合资源分配和功率控制的方法,用以实现高能效的D2D通信.仿真结果表明:本文提出的迭代资源分配和功率控制方案,相比已有方案能量效率有了明显提高.     

非线性互联大系统的最优控制:逐次逼近法

The optimal control problem for nonlinear interconnected large-scale dynamic systems is considered. A successive approximation approach for designing the optimal controller is proposed with respect to quadratic performance indexes. By using the approach, the high order, coupling, nonlinear two-point boundary value (TPBV) problem is transformed into a sequence of linear decoupling TPBV problems. It is proven that the TPBV problem sequence uniformly converges to the optimal control for nonlinear interconnected large-scale systems. A suboptimal control law is obtained by using a finite iterative result of the optimal control sequence.     

基于QoS保障的蜂窝-D2D系统功率控制算法

在频谱资源稀缺的无线网络通信中,蜂窝-设备到设备(D2D)复用模式被认为是有效提升了用户服务质量的关键技术之一.研究了蜂窝-D2D模式下的功率控制问题,在保证用户服务质量(QoS)约束的同时,有效地提升了系统吞吐量.     

Power Control Algorithm for Wireless Cellular System

A novel distributed power control algorithm based on interference estimation is presented for wireless cellular system. A classical result of stochastic approximation is applied in this scheme. The power control algorithm is converted to seeking for the zero point problem of a certain function. In this distributed power algorithm, each user iteratively updates its power level by estimating the interference. It does not require any knowledge of the channel gains or state information of other users. Hence, the proposed algorithm is robust. It is proved that the algorithm converges to the optimal solution by stochastic approximation approach.     

移动通信系统的功率控制

文中简述移动通信系统的干扰和与之相关的功率控制方法。     

通过功率控制建立密度自适应的分簇无线传感器网络

无线传感器网络的分簇密度是不定的,通过控制簇头发射功率将能使分簇密度控制在合理范围内.无线传感器网络的"簇头--成员"结构与CDMA的"基站--移动台"结构相似,可以采用CDMA的功率控制机制对无线传感器网络的最小ID分簇算法进行优化.改进后的算法和步骤得到了描述,并给出了控制簇头发射功率的流程图.     

一种结合功率控制的无线传感器网络区域定位算法

在无线传感器网络中,获取传感器节点的位置信息对于许多相关的应用十分重要.首先分析了现有的无线传感器网络定位算法,然后提出了一种结合功率控制的区域定位算法(ALPC),该算法将传感器节点的位置估计在一个小区域中,而不是寻找它的精确坐标,由sink节点完成复杂的计算,传感器节点无需进行大量的通信和计算,节省了能量并延长了网络的寿命;根据实测数据讨论了时空域对信号传播模型的影响,以及这种影响如何改变定位粒度;利用区域命名机制减小了网络流量负载提高了网络的可靠性;最后,通过仿真验证了算法的有效性.     

无线传感器网络的链路稳定成簇与功率控制算法

在能量有限条件下通过降低能耗来延长网络生存时间是无线传感器网络面临的重要挑战之一。在层次体系结构中,MAC层和网络层的能耗是影响系统能量有效性的关键,因此可以将这两层结合起来考虑,从网络跨层优化的角度来分析其能耗。针对现有典型成簇算法理论前提条件多、无法适应网络动态变化、不易在实际环境中实现的不足,结合功率控制理论及算法,基于跨层优化的策略提出了一种易于实现、能动态适应网络变化、能量有效的链路稳定成簇算法。该算法能在降低能耗的同时扩大网络的吞吐量。实验仿真结果表明,与现有的几种典型方案相比,新算法在保证网络稳定性的同时使网络具有了更大的有效吞吐量及更长的生存时间。     

基于电动助力转向系统控制算法的研究

为了达到对转向系统控制转向轻便又不失路感的目的,通过对汽车电动助力转向系统进行力学分析,建立数学模型,提出一种模糊控制与PD控制并行的控制算法。在大转矩时采用模糊PD控制,动态响应好,转向轻便,在小转矩是采用PD控制,控制精度高,不失路感。通过MATLAB进行仿真。研究结果表明,通过采用提出的控制策略即提高了系统的控制精度,又减小了系统的超调,缩短了调整时间,能获较理想的转向助力效果和良好的跟随性能。     

测控系统常用的几种电源变换电路

简要论述了测控系统的供电特点及实现方案,并介绍了一些测控系统常用的电源变换电路。     

核反应堆功率控制系统设计与仿真

目前国内核反应堆控制技术存在数字化和智能化程度不高的问题,为提高其安全与操作性能,设计了一种新型数字式反应堆功率控制系统。系统采用TMS320LF2812阵列作为控制器,应用改进PID算法实现三闭环功率调节,提高了功率控制系统的动态特性。给出了该方案系统框图、硬件框图,此设计具有结构紧凑、性能可靠、抗干扰性强等优点。并通过数学建模,构造出系统控制模型,再应用MATLAB仿真。仿真结果表明系统动态特性得到改善,完全能够满足反应堆安全运行需要。     

基于纳什均衡的D2D通信功率控制博弈算法

为了减轻D2D通信在资源复用模式下的互干扰问题,提升蜂窝网络均衡性能收益,提出一种基于纳什均衡的功率控制博弈算法。算法中将互干扰用户间的功率控制过程描述为静态博弈模型,用户之间根据最小化代价函数的博弈决策,通过多步迭代调节发射功率,使系统收敛至纳什均衡的优化状态。在用户代价函数设计中,综合考虑了能耗及传输速率影响,同时给出了博弈算法纳什均衡存在性以及收敛性的证明。仿真实验表明,在最优响应策略及能耗因子的有效约束下,互干扰用户更理智的选择发射功率,系统拥有较好均衡性收益的同时能耗进一步降低。     

An Adaptive Wireless Power Sharing Control for Multiterminal HVDC

Power sharing among multiterminal high voltage direct current terminals (MT-HVDC) is mainly developed based on a priority or sequential manners, which uses to prevent the problem of overloading due to a predefined controller coefficient. Furthermore, fixed power sharing control also suffers from an inability to identify power availability at a rectification station. There is a need for a controller that ensures an efficient power sharing among the MT-HVDC terminals, prevents the possibility of overloading, and utilizes the available power sharing. A new adaptive wireless control for active power sharing among multiterminal (MT-HVDC) systems, including power availability and power management policy, is proposed in this paper. The proposed control strategy solves these issues and, this proposed controller strategy is a generic method that can be applied for unlimited number of converter stations. The rational of this proposed controller is to increase the system reliability by avoiding the necessity of fast communication links. The test system in this paper consists of four converter stations based on three phase-two AC voltage levels. The proposed control strategy for a multiterminal HVDC system is conducted in the power systems computer aided design/electromagnetic transient design and control (PSCAD/EMTDC) simulation environment. The simulation results significantly show the flexibility and usefulness of the proposed power sharing control provided by the new adaptive wireless method.