Optimal and suboptimal packet scheduling over correlated time varying flat fading channels

We address the issue of optimal packet scheduling over correlated fading channels which trades off between minimization of three goals: average transmission power, average delay and average packet dropping probability. We show that the problem forms a weakly communicating Markov decision process and formulate the problem as both unconstrained and constrained problem. Relative value iteration (RVI) algorithm is used to find optimal deterministic policy for unconstrained problem, while optimal randomized policy for constrained problem is obtained using linear programming (LP) technique. Whereas with RVI only a finite number of scheduling policies can be obtained over the feasible delay region, LP can produce policies for all feasible delays with a fixed dropping probability and is computationally faster than the RVI. We show the structure of optimal deterministic policy in terms of the channel and buffer state and form a simple log functional suboptimal scheduler that approximately follows the optimal structure. Performance results are given for both constant and bursty Poisson arrivals, and the proposed suboptimal scheduler is compared with the optimal and channel threshold scheduler. Our suboptimal scheduler performs close to the optimal scheduler for every feasible delay and is robust to different channel parameters, number of actions and incoming traffic distributions.     

Training an Active Random Field for Real-Time Image Denoising

Many computer vision problems can be formulated in a Bayesian framework based on Markov random fields (MRF) or conditional random fields (CRF). Generally, the MRF/CRF model is learned independently of the inference algorithm that is used to obtain the final result. In this paper, we observe considerable gains in speed and accuracy by training the MRF/CRF model together with a fast and suboptimal inference algorithm. An active random field (ARF) is defined as a combination of a MRF/CRF based model and a fast inference algorithm for the MRF/CRF model. This combination is trained through an optimization of a loss function and a training set consisting of pairs of input images and desired outputs. We apply the ARF concept to image denoising, using the Fields of Experts MRF together with a 1-4 iteration gradient descent algorithm for inference. Experimental validation on unseen data shows that the ARF approach obtains an improved benchmark performance as well as a 1000-3000 times speedup compared to the Fields of Experts MRF. Using the ARF approach, image denoising can be performed in real-time, at 8 fps on a single CPU for a 256times256 image sequence, with close to state-of-the-art accuracy.     

Optimal Resource Allocation and Fairness Control in All-Optical WDM Networks

This paper investigates the problem of optimal wavelength allocation and fairness control in all-optical wavelength-division-multiplexing networks. A fundamental network topology, consisting of a two-hop path network, is studied for three classes of traffic. Each class corresponds to a source-destination pair. For each class, call interarrival and holding times are exponentially distributed. The objective is to determine a wavelength allocation policy in order to maximize the weighted sum of users of all classes (i.e., class-based utilization). This method is able to provide differentiated services and fairness management in the network. The problem can be formulated as a Markov decision process (MDP) to compute the optimal allocation policy. The policy iteration algorithm is employed to numerically compute the optimal allocation policy. It has been analytically and numerically shown that the optimal policy has the form of a monotonic nondecreasing switching curve for each class. Since the implementation of an MDP-based allocation scheme is practically infeasible for realistic networks, we develop approximations and derive a heuristic algorithm for ring networks. Simulation results compare the performance of the optimal policy and the heuristic algorithm, with those of complete sharing and complete partitioning policies.     

Paging and Registration in Cellular Networks: Jointly Optimal Policies and an Iterative Algorithm

This paper explores optimization of paging and registration policies in cellular networks. Motion is modeled as a discrete-time Markov process, and minimization of the discounted, infinite-horizon average cost is addressed. The structure of jointly optimal paging and registration policies is investigated through the use of dynamic programming for partially observed Markov processes. It is shown that there exist policies with a certain simple form that are jointly optimal. An iterative algorithm for policies with the simple form is proposed and investigated. The algorithm alternates between paging policy optimization, and registration policy optimization. It finds a pair of individually optimal policies. Majorization theory and Riesz's rearrangement inequality are used to show that jointly optimal paging and registration policies are given for symmetric or Gaussian random walk models by the nearest-location-first paging policy and distance threshold registration policies.     

ON MARKOV RANDOM FIELD MODELS FOR SEGMENTATION OF NOISY IMAGES

Markov random field(MRF) models for segmentation of noisy images are discussed. According to the maximum a posteriori criterion, a configuration of an image field is regarded as an optimal estimate of the original scene when its energy is minimized. However, the minimum energy configuration does not correspond to the scene on edges of a given image, which results in errors of segmentation. Improvements of the model are made and a relaxation algorithm based on the improved model is presented using the edge information obtained by a coarse-to-fine procedure. Some examples are presented to illustrate the applicability of the algorithm to segmentation of noisy images.     

A Markov random field model for classification of multisourcesatellite imagery

A general model for multisource classification of remotely sensed data based on Markov random fields (MRF) is proposed. A specific model for fusion of optical images, synthetic aperture radar (SAR) images, and GIS (geographic information systems) ground cover data is presented in detail and tested. The MRF model exploits spatial class dependencies (spatial context) between neighboring pixels in an image, and temporal class dependencies between different images of the same scene. By including the temporal aspect of the data, the proposed model is suitable for detection of class changes between the acquisition dates of different images. The performance of the proposed model is investigated by fusing Landsat TM images, multitemporal ERS-1 SAR images, and GIS ground-cover maps for land-use classification, and on agricultural crop classification based on Landsat TM images, multipolarization SAR images, and GIS crop field border maps. The performance of the MRF model is compared to a simpler reference fusion model. On an average, the MRF model results in slightly higher (2%) classification accuracy when the same data is used as input to the two models. When GIS field border data is included in the MRF model, the classification accuracy of the MRF model improves by 8%. For change detection in agricultural areas, 75% of the actual class changes are detected by the MRF model, compared to 62% for the reference model. Based on the well-founded theoretical basis of Markov random field models for classification tasks and the encouraging experimental results in our small-scale study, the authors conclude that the proposed MRF model is useful for classification of multisource satellite imagery     

D2F: A Routing Protocol for Distributed Data Fusion in Wireless Sensor Networks

Data fusion can be distributed into network and executed on network nodes, to reduce data from redundant sensor nodes, to fuse the information from complementary sensor nodes and to get the complete view from cooperative nodes. Consequently only the inference of interest is sent to end user. This distributed data fusion can significantly reduce the data transmission cost and there is no need for a powerful centralized node to process the collected information. However, to achieve the advantages of distributed data fusion and better utilization of network resources, each fusion function needs to be performed at particular network node for minimizing energy cost of data fusion application, both data transmission cost and computation cost. In this paper, distributed data fusion routing (D2F) is proposed, which is designed for deploying distributed data fusion application in wireless sensor networks. D2F can find the optimal route path and fusion placements for a given data fusion tree, which obtains the optimal energy consumption for in-network data fusion. D2F can also handle different link failures and maintain the optimality of energy cost of data fusion by adapting to the dynamic change of network.     

Information fusion Wiener filter for the multisensor multichannel ARMA signals with time-delayed measurements

For the multisensor multichannel autoregressive moving average (ARMA) signals with time-delayed measurements, a measurement transformation approach is presented, which transforms the equivalent state space model with measurement delays into the state space model without measurement delays, and then using the Kalman filtering method, under the linear minimum variance optimal weighted fusion rules, three distributed optimal fusion Wiener filters weighted by matrices, diagonal matrices and scalars are presented, respectively, which can handle the fused filtering, prediction and smoothing problems. They are locally optimal and globally suboptimal. The accuracy of the fuser is higher than that of each local signal estimator. In order to compute the optimal weights, the formulae of computing the cross-covariances among local signal estimation errors are given. A Monte Carlo simulation example for the three-sensor target tracking system with time-delayed measurements shows their effectiveness.     

Energy‐delay‐aware caching strategy in green CCN using markov approximation

One of the basic challenges in content‐centric networking (CCN) is how to optimize the overall energy consumption of content transmission and caching. Furthermore, designing an appropriate caching policy that considers both energy consumption and quality of service (QoS) is a major goal in green CCN. In this paper, the problem of minimizing the total CCN energy consumption while being aware of the end‐to‐end delay is formulated as an integer linear programming model. Since it is an Non‐deterministic Polynomial‐time (NP)‐hard problem, the Markov approximation method for an energy‐delay aware caching strategy (MAEDC) is proposed through a log‐sum‐exp function to find a near‐optimal solution in a distributed manner. The numerical results show that the MAEDC achieves near‐optimal energy consumption with better delay profile compared with the optimal solution. Moreover, due to the possibility of distributed and parallel processing, the proposed method is proper for the online situation where the delay is a crucial issue.     

Multiscale representations of Markov random fields

Recently, a framework for multiscale stochastic modeling was introduced based on coarse-to-fine scale-recursive dynamics defined on trees. This model class has some attractive characteristics which lead to extremely efficient, statistically optimal signal and image processing algorithms. The authors show that this model class is also quite rich. In particular, they describe how 1-D Markov processes and 2-D Markov random fields (MRFs) can be represented within this framework. The recursive structure of 1-D Markov processes makes them simple to analyze, and generally leads to computationally efficient algorithms for statistical inference. On the other hand, 2-D MRFs are well known to be very difficult to analyze due to their noncausal structure, and thus their use typically leads to computationally intensive algorithms for smoothing and parameter identification. In contrast, their multiscale representations are based on scale-recursive models and thus lead naturally to scale-recursive algorithms, which can be substantially more efficient computationally than those associated with MRF models. In 1-D, the multiscale representation is a generalization of the midpoint deflection construction of Brownian motion. The representation of 2-D MRFs is based on a further generalization to a “midline” deflection construction. The exact representations of 2-D MRFs are used to motivate a class of multiscale approximate MRF models based on one-dimensional wavelet transforms. They demonstrate the use of these latter models in the context of texture representation and, in particular, they show how they can be used as approximations for or alternatives to well-known MRF texture models     

A call admission control for service differentiation and fairness management in WDM grooming networks

We investigate a call admission control (CAC) mechanism for providing fairness control and service differentiation in a WDM network with grooming capabilities. A WDM grooming network can handle different classes of traffic streams which differ in their bandwidth requirements. We assume that for each class, call interarrival and holding times are exponentially distributed. Using a Markov Decision Process approach, an optimal CAC policy is derived for providing fairness in the network. The Policy Iteration algorithm is used to numerically compute the optimal policy. Furthermore, we propose a heuristic decomposition algorithm with lower computational complexity and good performance. Simulation results compare the performance of our proposed policy with those of Complete Sharing and Complete Partitioning policies. Comparisons show that our proposed policy provides the best performance in most cases. Although this approach is motivated by WDM networks, it may be deployed to determine the optimal resource allocation in many problems in wireless and wired telecommunications systems.     

Optimal power and retransmission control policies for random access systems

We consider in this study dynamic control policies for slotted Aloha random access systems. New performance bounds are derived when random access is combined with power control for system optimization, and we establish the existence of optimal control approaches for such systems. We analyze throughput and delay when the number of backlogged users is known, where we can explicitly obtain optimal policies and analyze their corresponding performance using Markov Decision Process (MDP) theory with average cost criterion. For the realistic unknown-backlog case, we establish the existence of optimal backlog-minimizing policies for the same range of arrival rates as the ideal known-backlog case by using the theory of MDPs with Borel state space and unbounded costs. We also propose suboptimal control policies with performance close to the optimal without sacrificing stability. These policies perform substantially better than existing "Certainty Equivalence" controllers.     

Optimal, suboptimal, and adaptive threshold policies for power efficiency of wireless networks

We prove that for any modulation, demodulation, and coding schemes and under general assumptions on the wireless channel model, the optimal power-efficient policy for transmit power assignment is necessarily of threshold nature. Although detailed features of this policy are quite complicated, it admits a simple and practical suboptimal version. Since both the optimal and suboptimal policies may have poor location-fairness characteristics, we introduce novel performance metrics (e.g., the average downtime and uptime), which quantify the short-term behavior of these policies, and develop analytical methods for their evaluation. Using these methods, we show that, although threshold policies may be extremely power efficient, users in poor locations relative to the base station may have unacceptable short-term performance characteristics. To alleviate this deficiency, we introduce an adaptive threshold policy, in which the threshold is adjusted to the user's location, and show that it is both relatively power efficient and location fair.     

Energy-Efficient SINR-Based Routing for Multihop Wireless Networks

In this paper, we develop an energy-efficient routing scheme that takes into account the interference created by existing flows in the network. The routing scheme chooses a route such that the network expends the minimum energy satisfying with the minimum constraints of flows. Unlike previous works, we explicitly study the impact of routing a new flow on the energy consumption of the network. Under certain assumptions on how links are scheduled, we can show that our proposed algorithm is asymptotically (in time) optimal in terms of minimizing the average energy consumption. We also develop a distributed version of the algorithm. Our algorithm automatically detours around a congested area in the network, which helps mitigate network congestion and improve overall network performance. Using simulations, we show that the routes chosen by our algorithm (centralized and distributed) are more energy efficient than the state of the art.     

Discrete Markov image modeling and inference on the quadtree

Noncasual Markov (or energy-based) models are widely used in early vision applications for the representation of images in high-dimensional inverse problems. Due to their noncausal nature, these models generally lead to iterative inference algorithms that are computationally demanding. In this paper, we consider a special class of nonlinear Markov models which allow one to circumvent this drawback. These models are defined as discrete Markov random fields (MRF) attached to the nodes of a quadtree. The quadtree induces causality properties which enable the design of exact, noniterative inference algorithms, similar to those used in the context of Markov chain models. We first introduce an extension of the Viterbi algorithm which enables exact maximum a posteriori (MAP) estimation on the quadtree. Two other algorithms, related to the MPM criterion and to Bouman and Shapiro's (1994) sequential-MAP (SMAP) estimator are derived on the same hierarchical structure. The estimation of the model hyper parameters is also addressed. Two expectation-maximization (EM)-type algorithms, allowing unsupervised inference with these models are defined. The practical relevance of the different models and inference algorithms is investigated in the context of image classification problem, on both synthetic and natural images.     

用马尔科夫模型优化分布式最小连通支配集算法

 为了提高无线传感器网络(WSNs)的能量利用效率、延长网络的生存时间,对基于极大独立集的最小连通支配集算法(MISB)进行优化,提出了一种新的算法.本文首先应用离散马尔科夫链为节点建立模型,并且根据模型预测节点的能量消耗;本算法进行多轮选举,每一轮开始时根据节点的度和能量选举支配点,依据模型预测的能量消耗决定本轮的运行时间,本轮运行结束时从新选举支配点,开始新一轮.仿真结果表明,本算法和原算法相比可以更好地平衡网络的能量消耗,提高全网的能量利用率,极大地延长网络的生存时间.     

Optimizing the transmit power for slow fading channels

We consider the design of power-adaptive systems for minimizing the average bit-error rate over flat fading channels. Channel state information, obtained through estimation at the receiver, is sent to the transmitter over a feedback channel, where it is used to optimally adapt the transmit power. We consider finite-state optimal policies to reflect the limitations of the feedback channel. We develop an iterative algorithm that determines the optimal finite-state power control policy given the probability density function (PDF) of the fading. Next, we present a discretized formulation of the problem and obtain a suboptimal solution via standard dynamic programming techniques. The discretization of the problem enables us to obtain a suboptimal policy for arbitrary fading channels for which the analytic expression of the fading probability density function is not available. Simulation results are used to draw conclusions regarding the effects of limited feedback channel capacity, delay and number of states on the bit-error rate performance of the proposed policies under slow and moderate fading conditions     

On zero-error source coding with decoder side information

Let (X,Y) be a pair of random variables distributed over a finite product set V/spl times/W according to a probability distribution P(x,y). The following source coding problem is considered: the encoder knows X, while the decoder knows Y and wants to learn X without error. The minimum zero-error asymptotic rate of transmission is shown to be the complementary graph entropy of an associated graph. Thus, previous results in the literature provide upper and lower bounds for this minimum rate (further, these bounds are tight for the important class of perfect graphs). The algorithmic aspects of instantaneous code design are considered next. It is shown that optimal code design is NP-hard. An optimal code design algorithm is derived. Polynomial-time suboptimal algorithms are also presented, and their average and worst case performance guarantees are established.     

分布式无线传感网络的协作目标跟踪策略

基于图像序列的目标跟踪是目标跟踪的重要研究问题之一.由于受图像解析度和跟踪范围限制,单视角跟踪准确性和鲁棒性不足.本文提出了分布式无线传感网络测量环境下的多视角协作融合跟踪方法,并引入了渐进分布式数据融合,采用基于能耗参数和信息有效性参数的综合优化函数动态选择融合节点,规划融合过程,平衡融合精度与网络能耗.通过目标跟踪准确性、网络能耗及传输延时对比实验表明:基于渐进分布式数据融合的协作信号处理方法提高了分布式多视角跟踪的准确性与实时性,减少了网络拥塞,降低了通讯能耗及延时.     

并行MCMC算法的SAR影像分割

MCMC(Markov Chain Monte Carlo, MCMC)方法采用顺序改变表征像素类属性的标号变量值会导致算法运算时间长、收敛速度慢等问题。为此,本文提出并行化改变像素标号值的MCMC方案,在贝叶斯推理框架下,依据高斯分布及MRF(Markov Random Field, MRF)模型建立SAR(Synthetic Aperture Radar, SAR)影像分割模型,设计实现基于多线程的并行采样方案;为了解决MRF标号场中邻域像素标号相关性问题,提出独立的像素并行采样的准则;同时,限制并行线程的数量,以保证采样的随机性。运用传统的串行算法和提出的并行算法对模拟和真实SAR影像进行影像分割实验;定性和定量的时间和精度评价结果表明:该方案在不影响分割精度的前提下大幅缩短影像分割时间,提高了效率。