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.     

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.     

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.     

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.     

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.     

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.     

A stochastic Kaczmarz algorithm for network tomography

We develop a stochastic approximation version of the classical Kaczmarz algorithm that is incremental in nature and takes as input noisy real time data. Our analysis shows that with probability one it mimics the behavior of the original scheme: starting from the same initial point, our algorithm and the corresponding deterministic Kaczmarz algorithm converge to precisely the same point. The motivation for this work comes from network tomography where network parameters are to be estimated based upon end-to-end measurements. Numerical examples via Matlab based simulations demonstrate the efficacy of the algorithm.     

Nonlinear methods for inverse statistical problems

In the uncertainty treatment framework considered, the intrinsic variability of the inputs of a physical simulation model is modelled by a multivariate probability distribution. The objective is to identify this probability distribution-the dispersion of which is independent of the sample size since intrinsic variability is at stake-based on observation of some model outputs. Moreover, in order to limit the number of (usually burdensome) physical model runs inside the inversion algorithm to a reasonable level, a nonlinear approximation methodology making use of Kriging and a stochastic EM algorithm is presented. It is compared with iterated linear approximation on the basis of numerical experiments on simulated data sets coming from a simplified but realistic modelling of a dyke overflow. Situations where this nonlinear approach is to be preferred to linearisation are highlighted.     

Simulation-based optimal sensor scheduling with application to observer trajectory planning

The sensor scheduling problem can be formulated as a controlled hidden Markov model and this paper solves the problem when the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. The aim is to minimise the variance of the estimation error of the hidden state w.r.t. the action sequence. We present a novel simulation-based method that uses a stochastic gradient algorithm to find optimal actions.     

Strategy Under the Unknown Stochastic Environment: the Nonparametric Lob—Pass Problem

The bandit problem consists of two factors, one being exploration or the collection of information on the environment and the other being the exploitation or taking benefit by choosing the optimal action in the uncertain environment. It is desirable to choose only the optimal action for the exploitation, while the exploration or collection of information requires taking a variety of (nonoptimal) actions as trials. Hence, in order to obtain the maximal cumulative gain, we need to compromise between the exploration and exploitation processes. We treat a situation where our actions change the structure of the environment, of which a simple example is formulated as the lob—pass problem by Abe and Takeuchi. Usually, the environment is specified by a finite number of unknown parameters in the bandit problem, so that the information collection part is to estimate their true values. This paper treats a more realistic situation of nonparametric estimation of the environment structure which includes an infinite number (a functional degree) of unknown parameters. A strategy is given under such a circumstance, proving that the cumulative regret can be made of the order O(log t) , O((log t) ² ) , or O(t ^1-σ ) (0< σ <1) depending on the dynamics of the environment, where t is the number of trials, in contrast with the optimal order O(log t) in the parametric case. Received December 14, 1996; revised June 14, 1997, and July 24, 1997.