An efficient communication relay placement algorithm for content‐centric wireless mesh networks

In this paper, we investigate a communication relay placement problem to optimize the network throughput in a content‐centric wireless mesh networks (WMN), in which the WMN is enhanced by including a small set of communication relays and a subset of wireless mesh routers serving as storage nodes. Specifically, we first define the communication relay placement problem in content‐centric WMNs. We then model the problem as a mathematical programming and propose a linear programming approach for calculating the achievable network throughput when the positions of communication relays are fixed. Next, to optimally placing the communication relays, we formulate an integer linear programming problem and we develop an efficient near‐optimal approximation algorithm based on linear programming relaxation. Finally, extensive simulation experiments have been conducted, and the results demonstrate the effectiveness of the proposed algorithms. Copyright © 2013 John Wiley & Sons, Ltd.     

Development of a Hybrid Algorithm for Efficient Optimisation of Base Station Placement for Indoor Wireless Communication Systems

A new hybrid algorithm termed Reduction Estimation—Combinatorial Optimisation—Reduction Approximation is proposed to identify an optimal base station placement as a subset of a known set of potential sites to provide wireless communication services to a given set of stationary users. Both forward and reverse link signal-to-interference ratios are considered, and the performance of the proposed algorithm is reported for several representative case studies and compared with Brute Force Search and existing optimisation algorithms (including Genetic, Ngadiman and Greedy algorithms). For the cases considered, the proposed algorithm is found to be superior to the existing algorithms in that it can yield an optimal deployment (equivalent to Brute Force Search) in an acceptable time.     

System Capacity Analysis and Antenna Placement Optimization for Downlink Transmission in Distributed Antenna Systems

System capacity and antenna placement play crucial roles in wireless communication systems, and they are of great value to network planning. In this paper, we are motivated to analyze the system capacity and optimize the antenna placement in distributed antenna systems. This paper establishes a composite channel model which takes path loss, lognormal shadowing and Rayleigh fading into consideration. To reduce the computational complexity, an approximate theoretical expression of system capacity is derived with selective transmission at the transmitter and maximal ratio combining at the receiver. An antenna placement optimization problem is formulated, and then a genetic algorithm (GA) based searching scheme is proposed to solve the proposed optimization problem. The computational complexity analysis indicates that the proposed GA-based searching scheme is computationally efficient in terms of both running time and storage space. Numerical results show that the approximate theoretical expression of system capacity can provide a very good approximation to the simulation results, and the proposed GA-based searching scheme for solving the antenna placement optimization problem can consistently offer a large capacity gain over other existing schemes.     

无线传感器网络中用于目标跟踪的节点规划算法

利用无线传感器网络进行目标跟踪时,由于各传感器节点的能量有限,数据蕴含的有效信息又各不相同,因此有必要规划参与目标跟踪的节点集和参与方式,以降低系统开销。本文提出了一种新的基于领导节点的节点规划算法,综合考虑收集数据和领导节点迁移过程中的通信开销,以最大化目标跟踪的性能。求解中以跟踪过程中的误差矩阵作为目标度量,采用高斯-赛德尔（Gauss-Seidel）和凸松弛等方法,使得复杂的带约束优化问题能够在接近O（N3）的时间复杂度内得到求解。仿真结果表明,与对比算法相比,本算法在相同的通信能量约束下能够达到更好的跟踪性能。     

Polynomial time optimal algorithms for time slot assignment ofvariable bandwidth systems

Considers the optimal (i.e., minimum length) time slot assignment problem for variable bandwidth switching systems. Existing algorithms for this problem are known to be pseudo-polynomial. The practical question of finding a fast optimal algorithm, as well as the theoretical question of whether the above problem is NP-complete were left open. The authors present a technique to show polynomial time complexity of some time slot assignment algorithms. Such a technique applies to an algorithm proposed by Chalasani and Varma in 1991 (called the CV algorithm), as well as to a network flow based optimal algorithm, proposed in the present paper for the first time. The CV algorithm and the one proposed are slightly different. Thus, the authors give an answer to both the above questions, by establishing that the problem is in P, and by showing effective algorithms for it     

An evolutive framework for server placement optimization to digital twin networks

Digital twin network (DTN) is a foremost enabler for efficient optimization in modern networks, as it owns massive real-time data and requires interaction with the physical network in real-time. When constructing a DTN, it is necessary to deploy many servers in the physical network for digital models' storage, calculation, and communication. Evolutionary algorithms show outstanding global optimization capabilities compared to the constructive heuristic method in such an optimization problem. However, due to the high dimensionality of the problem and the complicated evaluation of the deployment plan, evolutionary algorithms easily fall into the optimum local at a high computational cost, given that the server placement problem is an NP-hard combinatorial optimization problem. In this research, we propose an evolutionary framework for server layout optimization that significantly improves the optimization efficiency of evolutionary algorithms and reduces the algorithm's computational cost. An offline-learning-based approach is used to reduce the search space, and a self-examining guided local search method is proposed to improve the search efficiency. Additionally, a look-up table-based hybrid approach is used for solution evaluation, reducing computational overhead. Experimental results show that the proposed framework and optimization strategy can significantly improve the evolutionary algorithm search efficiency and achieve excellent convergence performance.     

Wavelength converter placement under different RWA algorithms in wavelength-routed all-optical networks

Sparse wavelength conversion and appropriate routing and wavelength assignment (RWA) algorithms are the two key factors in improving the blocking performance in wavelength-routed all-optical networks. It has been shown that the optimal placement of a limited number of wavelength converters in an arbitrary mesh network is an NP-complete problem. There have been various heuristic algorithms proposed in the literature, in which most of them assume that a static routing and random-wavelength assignment RWA algorithm is employed. However, the existing work shows that fixed-alternate routing and dynamic routing RWA algorithms can achieve much better blocking performance. Our study further demonstrates that the wavelength converter placement and RWA algorithms are closely related in the sense that a well-designed wavelength converter placement mechanism for a particular RWA algorithm might not work well with a different RWA algorithm. Therefore, the wavelength converter placement and the RWA have to be considered jointly. The objective of this paper is to investigate the wavelength converter placement problem under the fixed-alternate routing (FAR) algorithm and least-loaded routing (LLR) algorithm. Under the FAR algorithm, we propose a heuristic algorithm called minimum blocking probability first for wavelength converter placement. Under the LLR algorithm, we propose another heuristic algorithm called weighted maximum segment length. The objective of the converter placement algorithms is to minimize the overall blocking probability. Extensive simulation studies have been carried out over three typical mesh networks, including the 14-node NSFNET, 19-node EON, and 38-node CTNET. We observe that the proposed algorithms not only outperform existing wavelength converter placement algorithms by a large margin, but they also can achieve almost the same performance compared with full wavelength conversion under the same RWA algorithm.     

Scalable service migration in autonomic network environments

Service placement is a key problem in communication networks as it determines how efficiently the user service demands are supported. This problem has been traditionally approached through the formulation and resolution of large optimization problems requiring global knowledge and a continuous recalculation of the solution in case of network changes. Such approaches are not suitable for large-scale and dynamic network environments. In this paper, the problem of determining the optimal location of a service facility is revisited and addressed in a way that is both scalable and deals inherently with network dynamicity. In particular, service migration which enables service facilities to move between neighbor nodes towards more communication cost-effective positions, is based on local information. The migration policies proposed in this work are analytically shown to be capable of moving a service facility between neighbor nodes in a way that the cost of service provision is reduced and - under certain conditions - the service facility reaches the optimal (cost minimizing) location, and locks in there as long as the environment does not change; as network conditions change, the migration process is automatically resumed, thus, naturally responding to network dynamicity under certain conditions. The analytical findings of this work are also supported by simulation results that shed some additional light on the behavior and effectiveness of the proposed policies.     

Energy efficient power allocation strategy for downlink MU-MIMO with massive antennas

With the increasing energy consumption, energy efficiency （EE） has been considered as an important metric for wireless communication networks as spectrum efficiency （SE）. In this paper, EE optimization problem for downlink multi-user multiple-input multiple-output （MU-MIMO） system with massive antennas is investigated. According to the convex optimization theory, there exists a unique globally optimal power allocation achieving the optimal EE, and the closed-form of the optimal EE only related to channel state information is derived analytically. Then both the approximate and accurate power allocation algorithms with different complexity are proposed to achieve the optimal EE. Simulation results show that the optimal EE obtained by the approximate algorithm coincides to that achieved by the accurate algorithm within the controllable error limitation, and these proposed algorithms perform better than the existing equal power allocation algorithm. The optimal EE and corresponding SE increase with the number of antennas at base station, which is promising for the next generation wireless communication networks.     

A cellular coevolutionary algorithm for image segmentation

Clustering is inherently a difficult problem, both with respect to the definition of adequate models as well as to the optimization of the models. We present a model for the cluster problem that does not need knowledge about the number of clusters a priori . This property is among others useful in the image segmentation domain, which we especially address. Further, we propose a cellular coevolutionary algorithm for the optimization of the model. Within this scheme multiple agents are placed in a regular two-dimensional (2-D) grid representing the image, which imposes neighboring relations on them. The agents cooperatively consider pixel migration from one agent to the other in order to improve the homogeneity of the ensemble of the image regions they represent. If the union of the regions of neighboring agents is homogeneous then the agents form alliances. On the other hand, if an agent discovers a deviant subject, it isolates the subject. In the experiments we show the effectiveness of the proposed method and compare it to other segmentation algorithms. The efficiency can easily be improved by exploiting the intrinsic parallelism of the proposed method.     

Fast optimal video placement algorithms for hierarchicalvideo-on-demand systems

The multilevel hierarchical network architecture has been shown to be a scalable and cost efficient solution for large video-on-demand (VOD) systems. The predominant operation cost of a hierarchical VOD system consists of network transmission cost and video storage cost. How to minimize the operation cost under several operating constraints is an important issue. Many operating constraints, such as the storage capacity limitation at each level of servers, have made the problem intractable. We proposed several efficient heuristic video placement algorithms that can achieve near optimal operating cost. We have also proposed a time-variant arrival traffic model with arrival rate matching the statistics gathered from commercial systems     

Algorithm of conditional minimization of the goal function for optimal routing in information networks

Anew algorithm has been proposed for solving the optimal routing problem. This algorithm is based on applying the Kirchhoff laws to information networks and does not require the mandatory use of derivatives of the goal function making it quite convenient for distributed realizations. The algorithm convergence is substantiated by drawing an analogy between information and electric networks. On the basis of a case study of the network it was shown that its speed is tens of times as high as that of the flow deviation algorithm. It was shown that theoretical labor intensity of implementing this method is substantially less than that of the algorithms based on finding the shortest routes, since the cyclic part of this algorithm does not contain laborious logical operations.     

特征选择中B&B算法的改进及比较

B&B(Branch & Bound)算法是特征选择中的一种全局最优算法,其固有缺点是运行时间太长.用B&B算法构造一棵搜索树,在树中搜索最优的特征子集.对B&B算法的研究集中在化简搜索树从而降低搜索复杂度上,提出了几种改进的B&B算法.从原理上分析了B&B算法及其各种改进的优缺点,将这一系列算法纳入到同一个算法框架,并在此基础上提出了一种针对BBPP算法的改进算法,BBPP+算法.通过比较各种实验数据,发现改进后的BBPP+算法的运行效率比已有的B&B算法更好.     

A Lion‐Whale optimization‐based migration of virtual machines for data centers in cloud computing

The scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service‐level agreement violation. In this paper, a novel VMM algorithm based on Lion‐Whale optimization is developed by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale VMM based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. The performance of the proposed method is validated over existing optimization‐based VMM algorithms, such as particle swarm optimization and genetic algorithm, using the performance measures, such as energy consumption, migration cost, and resource use. Simulation results reveal the fact that the proposed Lion‐Whale VMM effectively outperforms other existing approaches in optimal VM placement for cloud computing environment with reduced migration cost of 0.01, maximal resource use of 0.36, and minimal energy consumption of 0.09.     

Efficient Cache Placement in Multi-Hop Wireless Networks

In this paper, we address the problem of efficient cache placement in multi-hop wireless networks. We consider a network comprising a server with an interface to the wired network, and other nodes requiring access to the information stored at the server. In order to reduce access latency in such a communication environment, an effective strategy is caching the server information at some of the nodes distributed across the network. Caching, however, can imply a considerable overhead cost; for instance, disseminating information incurs additional energy as well as bandwidth burden. Since wireless systems are plagued by scarcity of available energy and bandwidth, we need to design caching strategies that optimally trade-off between overhead cost and access latency. We pose our problem as an integer linear program. We show that this problem is the same as a special case of the connected facility location problem, which is known to be NP-hard. We devise a polynomial time algorithm which provides a suboptimal solution. The proposed algorithm applies to any arbitrary network topology and can be implemented in a distributed and asynchronous manner. In the case of a tree topology, our algorithm gives the optimal solution. In the case of an arbitrary topology, it finds a feasible solution with an objective function value within a factor of 6 of the optimal value. This performance is very close to the best approximate solution known today, which is obtained in a centralized manner. We compare the performance of our algorithm against three candidate cache placement schemes, and show via extensive simulation that our algorithm consistently outperforms these alternative schemes.     

Novel cooperative neural fusion algorithms for image restoration and image fusion.

To deal with the problem of restoring degraded images with non-Gaussian noise, this paper proposes a novel cooperative neural fusion regularization (CNFR) algorithm for image restoration. Compared with conventional regularization algorithms for image restoration, the proposed CNFR algorithm can relax need of the optimal regularization parameter to be estimated. Furthermore, to enhance the quality of restored images, this paper presents a cooperative neural fusion (CNF) algorithm for image fusion. Compared with existing signal-level image fusion algorithms, the proposed CNF algorithm can greatly reduce the loss of contrast information under blind Gaussian noise environments. The performance analysis shows that the proposed two neural fusion algorithms can converge globally to the robust and optimal image estimate. Simulation results confirm that in different noise environments, the proposed two neural fusion algorithms can obtain a better image estimate than several well known image restoration and image fusion methods.     

基于多智能体深度强化学习的D2D通信资源联合分配方法

设备对设备(D2D)通信作为一种短距离通信技术,能够极大地减轻蜂窝基站的负载压力和提高频谱利用率。然而将D2D直接部署在授权频段或者免授权频段必然导致与现有用户的严重干扰。当前联合部署在授权和免授权频段的D2D通信的资源分配通常被建模为混合整数非线性约束的组合优化问题,传统优化方法难以解决。针对这个挑战性问题,该文提出一种基于多智能体深度强化学习的D2D通信资源联合分配方法。在该算法中,将蜂窝网络中的每个D2D发射端作为智能体,智能体能够通过深度强化学习方法智能地选择接入免授权信道或者最优的授权信道并发射功率。通过选择使用免授权信道的D2D对(基于“先听后说”机制)向蜂窝基站的信息反馈,蜂窝基站能够在非协作的情况下获得WiFi网络吞吐量信息,使得算法能够在异构环境中执行并能够确保WiFi用户的QoS。与多智能体深度Q网络(MADQN)、多智能体Q学习(MAQL)和随机算法相比,所提算法在保证WiFi用户和蜂窝用户的QoS的情况下能够获得最大的吞吐量。     

Relax online resource allocation algorithms for D2D communication

Maximizing the system sumrate by sharing the resource blocks among the cellular user equipments and the D2D (device to device) pairs while maintaining the quality of service is an important research question in a D2D communication underlaying cellular networks. The problem can be optimally solved in offline by using the weighted bipartite matching algorithm. However, in long‐term evolution and beyond (4G and 5G) systems, scheduling algorithms should be very efficient where the optimal algorithm is quite complex to implement. Hence, a low complexity algorithm that returns almost the optimal solution can be an alternative to this research problem. In this paper, we propose 2 less complex stable matching–based relax online algorithms those exhibit very close to the optimal solution. Our proposed algorithms deal with fixed number of cellular user equipments and a variable number of D2D pairs those arrive in the system online. Unlike online matching algorithms, we consider that an assignment can be revoked if it improves the objective function (total system sumrate). However, we want to minimize the number of revocation (ie, the number of changes in the assignments) as a large number of changes can be expensive for the networks too. We consider various offline algorithms proposed for the same research problem as relaxed online algorithms. Through extensive simulations, we find that our proposed algorithms outperform all of the algorithms in terms of the number of changes in assignment between 2 successive allocations while maintaining the total system sumrate very close to the optimal algorithm.     

最大流算法应用于二次线性规划布局合法化过程

以力导向为基础的解析型算法如今越来越多地被应用到FPGA布局问题当中去,二次线性规划算法便是其中一种,其使用数学的方法求解拉力模型矩阵,以得到理论的最优解.但在实际的算法实现当中,二次线性规划虽体现出了其较快求解的特性,其解却存有重叠的问题,尚需进一步合法化以解决重叠问题.现有的合法化过程一般较为随意,并无系统性算法,...     

Mobility aware edge service migration strategy

To address the problem of load imbalance among edge servers and quality of service degradation caused by dynamic changes of user locations in mobile edge computing networks,a mobility aware edge service migration algorithm was proposed.Firstly,the optimization problem was formulated as a mix integer nonlinear programming problem,with the goal of minimizing the perceived delay of user service request.Then,the delay optimization problem was decoupled into the edge service migration and edge node selection sub-problems based on the Lyapunov optimization approach.Thereafter,the fast edge decision algorithm was proposed to optimize the resource allocation and edge service migration under a given radio access strategy.Finally,the asynchronous optimal response algorithm was proposed to iterate out the optimal radio access strategy.Simulation results validate the proposed algorithm can reduce the perceived delay under the service migration cost constraint while comparing with other existing algorithms.