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Network planning and design

About: Network planning and design is a research topic. Over the lifetime, 12393 publications have been published within this topic receiving 229776 citations. The topic is also known as: network design.


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Proceedings ArticleDOI
01 Nov 2003
TL;DR: The present design allows us to instantiate arbitrary network topologies, has a low latency and high throughput, and is part of the platform the author is developing for reconfigurable systems.
Abstract: An efficient methodology for building the billion-transistors systems on chip of tomorrow is a necessity. Networks on chip promise to be the solution for the numerous technological, economical and productivity problems. We believe that different types of networks are required for each application domains. Our approach therefore is to have a very flexible network design, highly scalable, that allows to easily accommodate the various needs. This paper presents the design of our network on chip, which is part of the platform we are developing for reconfigurable systems. The present design allows us to instantiate arbitrary network topologies, has a low latency and high throughput.

72 citations

Journal ArticleDOI
TL;DR: In this article, the authors developed a route-space directed network and modeled the problem as a 0-1 binary program to minimize the total operating cost, while satisfying the desired level of service.
Abstract: Time-definite express common carriers provide time guaranteed door-to-door express service for small parcel shipments. Centers pick up and deliver parcels, while hubs consolidate partial loads. Each center is connected through a secondary route to its primary hub, while hubs are mutually connected by primary routes in a hierarchical hub-and-spoke network. The carriers may dispatch large trucks/aircraft on the primary routes but utilize smaller trucks/aircraft on the secondary routes. The time-constrained hierarchical hub-and-spoke network design problem involves determining the fleet size and schedules on the primary and secondary routes to minimize the total operating cost, while satisfying the desired level of service. We developed a route-space directed network and modeled the problem as a 0–1 binary program. An implicit enumeration method with an embedded least time path subproblem was developed. The sensitivity analysis on the service level in a partial line-haul operations network for the second largest carrier in Taiwan showed that the costs are not strictly monotonically increasing with the service levels, rather they are monotonically non-decreasing according to a step function. In addition, the determination of the sort start and pickup cutoff times has a great impact on the total cost.

72 citations

Journal ArticleDOI
TL;DR: NPO is formulated first as a Network Design Problem (NDP) and also as an Efficient Allocation Problem (EAP), where an optimal flow pattern, the System Optimum (SO), is sought and tolls are consistently determined.
Abstract: Network Pricing Optimization (NPO) is formulated first as a Network Design Problem (NDP) where the design variables are tolls, the objective function is the Social Surplus and the equilibrium constraint is any current multi-user multimodal stochastic traffic assignment model with elastic demand up to trip generation and asymmetric arc cost function Jacobian. NPO is then formulated also as an Efficient Allocation Problem (EAP), where an optimal flow pattern, the System Optimum (SO), is sought and tolls are consistently determined. Necessary and sufficient conditions for the solutions to both problems are stated, showing the validity of the marginal pricing principle in the context considered.

72 citations

BookDOI
27 Nov 2006
TL;DR: The author reveals how the design approach and economic considerations in this book and its follow-up, HSDPA, changed from a simple to a systematic approach to a more holistic approach to solve the challenges faced by today's network operators.
Abstract: List of contributors. Foreword. Preface. Acronyms. Acknowledgement. 1. INTRODUCTION TO UMTS NETWORKS (Patrick Chan, Andrea Garavaglia, Christophe Chevallier). 1.1 UMTS NETWORK TOPOLOGY. 1.2 WCDMA CONCEPTS. 1.3 WCDMA NETWORK DEPLOYMENT OPTIONS. 1.4 THE EFFECTS OF VENDOR IMPLEMENTATION. 2. RF PLANNING AND OPTIMIZATION (Christophe Chevallier). 2.1 INTRODUCTION. 2.2 OVERVIEW OF THE NETWORK DEPLOYMENT PROCESS. 2.3 LINK BUDGETS. 2.4 NETWORK PLANNING TOOLS. 2.5 INTERFERENCE CONSIDERATIONS DURING NETWORK PLANNING. 2.6 TOPOLOGY PLANNING. 2.7 PARAMETER SETTINGS AND OPTIMIZATION DURING NETWORK PLANNING. 2.8 RF OPTIMIZATION. 3. CAPACITY PLANNING AND OPTIMIZATION (Christophe Chevallier). 3.1 BASIC UMTS TRAFFIC ENGINEERING. 3.2 EFFECT OF VIDEO-TELEPHONY AND PS DATA ON TRAFFIC ENGINEERING. 3.3 MULTI-SERVICE TRAFFIC ENGINEERING. 3.4 CAPACITY PLANNING. 3.5 OPTIMIZING FOR CAPACITY. 4. INITIAL PARAMETER SETTINGS (Christopher Brunner, Andrea Garavaglia, Christophe Chevallier). 4.1 Introduction. 4.2 Physical Layer Parameters. 4.3 Intra-Frequency Cell Reselection Parameters. 4.4 Access Parameter Recommendations. 4.5 Intra-Frequency Handover Parameters. 5. SERVICE OPTIMIZATION (Andrea Forte, Patrick Chan, Christophe Chevallier). 5.1 KPI AND LAYERED OPTIMIZATION APPROACH. 5.2 VOICE SERVICE OPTIMIZATION. 5.3 VIDEO TELEPHONY SERVICE OPTIMIZATION. 5.4 PS DATA SERVICE OPTIMIZATION. 6. INTER-SYSTEM PLANNING AND OPTIMIZATION (Andrea Garavaglia, Christopher Brunner, Christophe Chevallier). 6.1 INTRODUCTION. 6.2 INTER-SYSTEM BOUNDARY PLANNING. 6.3 INTER-SYSTEM TRANSITIONS IN CONNECTED MODE. 6.4 INTER-SYSTEM TRANSITIONS IN IDLE MODE. 6.5 TEST SETUP FOR INTER-SYSTEM HANDOVER AND CELL RESELECTION PERFORMANCE ASSESSMENT. 6.6 OPTIMIZING INTER-SYSTEM PARAMETERS. 6.7 ADDITIONAL INTER-SYSTEM PLANNING AND OPTIMIZATION ISSUES. 7. HSDPA (Kevin Murray, Sunil Patil). 7.1 MOTIVATIONS FOR HSDPA. 7.2 HSDPA CONCEPTS. 7.3 HSDPA PLANNING. 7.4 HSDPA OPERATION AND OPTIMIZATION. 7.5 HSDPA KEY PERFORMANCE INDICATORS (KPI). 7.6 TEST SETUP. 8. INDOOR COVERAGE (Patrick Chan, Ken Baker, Christophe Chevallier). 8.1 INTRODUCTION. 8.2 DESIGN APPROACH AND ECONOMIC CONSIDERATIONS. 8.3 COVERAGE PLANNING AND IMPACT ON CAPACITY. 8.4 OPTIMIZING INDOOR SYSTEMS. Index.

72 citations

Journal ArticleDOI
01 Nov 2016
TL;DR: By two consecutive optimization, combining the advantages of three algorithms of PLS, GA, and RBF, a reliable small sample classification algorithm (PLS-GA-RBF) is established and has unique superiority in dealing with the small sample.
Abstract: Display OmittedThis paper's Graphical abstractWhen using the RBF neural network to deal with small samples with high feature dimension and few numbers, too many inputs are difficult to determine the numbers of hidden layer neurons, it influences the design structure of the network, the redundancies or correlative data will influence the training of the network, and relatively few number of samples make network train non-completed or over-fitted, thereby affecting the operating efficiency and recognition accuracy of neural network.For the problem of small sample classification, two aspects of RBF neural network are optimized. Firstly, the original data reduces their feature dimension by PLS algorithm, then the low dimensional data is used as network input, it regard as external optimization. And then, using genetic algorithm to optimize RBF, the optimization way adopts hybrid coding and simultaneous evolving for hidden layer neurons and connection weights, this step regard as internal optimization. By these two consecutive optimizations, an optimized RBF neural network algorithm based on PLS and GA (PLS-GA-RBF algorithm) for small sample is established, which facilitates the hidden layer of network design, and improves the network training speed and generalization ability, thereby improving the operating efficiency and recognition accuracy of the network.The new algorithm is ingenious combination of the advantages of three algorithms, it realize the external optimization by PLS and internal optimization by GA. PLS-GA-RBF algorithm can fit more complex nonlinear recognition problems, and is more suitable for the small sample classification, which with high feature dimension and fewer numbers.In order to verify the reliability of the PLS-GA-RBF algorithm, multiple instances is used to validate and analysis. In this paper, four different experiments are arranged; among them are three small sample test and one large sample test. The purpose of the arrangement large sample test is to compare of validation. The result is satisfactory, which means the new algorithm has unique superiority in dealing with the small sample. The nature of small sample is well-analyzed.PLS is employed to reduce feature dimension of small sample, which obtained the relatively ideal low-dimensional data as the inputs of neural network.Unlike previous studies, the optimized GA-RBF algorithm is adopts the way of hybrid coding and simultaneous evolving for hidden layer neurons and connection weights.By two consecutive optimization, combining the advantages of three algorithms of PLS, GA, and RBF, a reliable small sample classification algorithm (PLS-GA-RBF) is established.Four different groups of experiments are arranged to valuate the classification ability of PLS-GA-RBF algorithm. Radial basis function (RBF) neural network can use linear learning algorithm to complete the work formerly handled by nonlinear learning algorithm, and maintain the high precision of the nonlinear algorithm. However, the results of RBF would be slightly unsatisfactory when dealing with small sample which has higher feature dimension and fewer numbers. Higher feature dimension will influence the design of neural network, and fewer numbers of samples will cause network training incomplete or over-fitted, both of which restrict the recognition precision of the neural network. RBF neural network has some drawbacks, for example, it is hard to determine the numbers, center and width of the hidden layer's neurons, which constrain the success of training. To solve the above problems, partial least squares (PLS) and genetic algorithm(GA)are introduced into RBF neural network, and better recognition precision will be obtained, because PLS is good at dealing with the small sample data, it can reduce feature dimension and make low-dimensional data more interpretative. In addition, GA can optimize the network architecture, the weights between hidden layer and output layer of the RBF neural network can ease non-complete network training, the way of hybrid coding and simultaneous evolving is adopted, and then an accurate algorithm is established. By these two consecutive optimizations, the RBF neural network classification algorithm based on PLS and GA (PLS-GA-RBF) is proposed, in order to solve some recognition problems caused by small sample. Four experiments and comparisons with other four algorithms are carried out to verify the superiority of the proposed algorithm, and the results indicate a good picture of the PLS-GA-RBF algorithm, the operating efficiency and recognition accuracy are improved substantially. The new small sample classification algorithm is worthy of further promotion.

72 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202390
2022195
2021432
2020493
2019570
2018573