Network Optimization Process Research Articles

Optimization of submerged floating tunnel cross section based on parametric Bézier curves and hybrid backpropagation - genetic algorithm

The cross-section geometry of a submerged floating tunnel (SFT) has a large effect on hydrodynamic characteristics, structural behavior and service level, making the tunnel cross section the primary factor in optimizing efficiency. Minimizing the mean drag and the dynamic variability in the lift of the SFT cross section under bi-directional (i.e., tidal) flow has a dramatic impact on the reduction of structural displacements and mooring loads. Based on a parametric Bézier curve dynamically comprising the leading-edge radius, tunnel height and width to define the SFT geometry, a sensitivity analysis of the Bézier curve parameters for a fixed aspect ratio with prototype dimensions under uniform flow conditions was conducted by applying Computational Fluid Dynamics (CFD), and the pressure distribution around the SFT cross-section surface was analyzed. A theoretical method comprising the Kármán vortex street parameters was employed to verify the CFD simulation results. In order to determine the SFT cross section with optimal hydrodynamic properties, the mean drag and Root Mean Square (RMS) lift coefficients were selected as optimization objectives, and four Bézier curve parameters were the input variables, in a neural network and genetic algorithm optimization process (a hybrid BP-GA structure), which is less likely to become trapped in local minima. The results show the optimal tunnel cross section has a mean drag and a RMS lift coefficient reduced by 0.9% and 6.3%, respectively, compared to the original CFD dataset.

Intelligent User-Centric Networks: Learning-Based Downlink CoMP Region Breathing

In the presence of irregular transmission/reception point (TRP) topologies and non-uniform user distribution, the user-to-node association optimization is a rather challenging process in real user-centric networks, especially for the joint transmission aided coordinated multipoint (CoMP) technique. The grade of challenge further escalates, when taking the dynamic user scheduling process into account in order to enhance the system capacity attained. To tackle the above-mentioned problem, we holistically optimize the system by conceiving joint user scheduling and user-to-node association. Then, for the sake of striking a significantly better balance between the network capacity and coverage quality, we propose a generalized reinforcement learning assisted framework intrinsically amalgamated both with neural-fitted Q-iteration as well as with ensemble learning and transfer learning techniques. Consequently, a powerful policy can be found for dynamically adjusting the set of TRPs participating in the joint transmission, thus allowing the CoMP-region to breathe, depending on both the temporal and geographical distribution of the tele-traffic load across the network. To facilitate the prompt learning of the global policy supporting flexible scalability, the overall network optimization process is decoupled into multiple local optimization phases associated with a number of TRP clusters relying on iterative information exchange among them. Our simulation results show that the proposed scheme is capable of producing a policy achieving a network-edge throughput gain of up to 140%$ and a network capacity gain of up to 190% under the challenging scenario of having a non-uniform geographical UE distribution and bursty traffic.

A Method of Emergent Event Evolution Reasoning Based on Ontology Cluster and Bayesian Network

Comprehensive real-time event information is critical to policymakers during emergency response and decision-making process. However, the development process of the emergent events has great uncertainty, and situational evolutions of emergencies are often difficult to use the fixed reasoning mode to attain. For this reason, this paper proposes a new method based on the ontology cluster for the evolution reasoning of emergency scenarios and extends the sematic web rule language to realize the scenario deduction, which can apply the Bayesian network to perform the conditional probability reasoning. A counterpart modeling and modifying of the Bayesian network optimization process is introduced. Besides, the probabilistic interpretation rules of atom components in context evolution are described with detailed query examples of emergency situation deducting and reasoning. The experimental results show that this approach is efficient in describing and capable of calculating the occurrence possibilities of the emergent events.

A novel 3D ray launching technique for radio propagation prediction in indoor environments.

Radio propagation prediction simulation methods based on deterministic technique such as ray launching is extensively used to accomplish radio channel characterization. However, the superiority of the simulation depends on the number of rays launched and received. This paper presented the indoor three-dimensional (3D) Minimum Ray Launching Maximum Accuracy (MRLMA) technique, which is applicable for an efficient indoor radio wave propagation prediction. Utilizing the novel MRLMA technique in the simulation environment for ray lunching and tracing can drastically reduce the number of rays that need to be traced, and improve the efficiency of ray tracing. Implementation and justification of MRLMA presented in the paper. An indoor office 3D layouts are selected and simulations have been performed using the MRLMA and other reference techniques. Results showed that the indoor 3D MRLMA model is appropriate for wireless communications network systems design and optimization process with respect to efficiency, coverage, number of rays launching, number of rays received by the mobile station, and simulation time.

A proposal for the supply chain design: A digitization approach

The logistics network of an automotive company in Mexico, was analyzed to propose a better logistics network in the country to improve delivery times to customers. The network design considers elements of digitization of Greenfield Analysis and Network Optimization processes. Taking into account t

Optimization of LTE Network Coverage

The trend of mobile, broadband and IP is becoming more and more prominent. Mobile communication technology is now in acritical period in the evolution of network technology. As such, LTE (Long Term Evolution) comes to light. LTE serves as a unified standardfor the new generation of mobile communications, equipped with high spectral efficiency, high peak rate, high mobility, flat networkarchitecture and other advantages. Huawei has played a very important role in the commercialization of LTE. As a sponsor of NGMN,Huawei has been actively involved in the research of some key projects of NGMN, such as network self-optimization, system architectureand Network performance evaluation and so on. This paper mainly introduces the problems encountered in the optimization of LTEnetwork coverage, the practical problems encountered by the LTE project team in Qingdao and the analysis and solutions to the problems.At the same time, this paper discusses in detail on the LTE network optimization process.

Mobility-Aware Cell Clustering Mechanism for Self-Organizing Networks

Self-Organizing Networks (SON) which automate the mobile networks in a cost-efficient way can provide extensive benefits for mobile network operators that are facing several challenges, such as providing maximum coverage and balanced/efficient usage of spectrum and energy. However, a major challenge in future wireless cellular systems is the design of self-organizing architecture that can enable re-configurable, scalable, flexible, low-cost, and efficient solutions for supporting increasingly diverse applications, products, and services of user and network requirements. In this paper, we discuss the benefits and opportunities of mobility-aware clustering capability on different conventional SON functions and advanced Long-Term Evolution features. We further propose a novel cell clustering methodology, which utilizes handover attempt data and aims to collect the cells that have higher mobility activity with each other in a single cluster. One of the main benefits of the clustering mechanism is to reduce the complexity of network optimization processes via checking smaller number of cells instead of evaluating entire network, as their number is gradually increasing. We consider real-world data of evolved Node-Bs (eNodeBs) in one of the major cities while evaluating our novel clustering technique that considers the mobility activities and location information of eNodeBs. In our evaluations, ${k}$ -means clustering is used as a benchmark. The clustering technique provides the detection of the cells whose serving regions are overlapped. Our results indicate that up to %6 increment is achieved in number of the ratio of intra-cluster handover attempts to total handover attempts. Additionally, the results further show a reduction in the standard deviation of the number of cells within a single cluster with a ratio of %8, which yields more uniform distribution of the cells across different clusters.

Co-optimization of CO2-EOR and Storage Processes under Geological Uncertainty

This paper presents an integrated numerical framework to co-optimize EOR and CO2 storage performance in the Farnsworth field unit (FWU), Ochiltree County, Texas. The framework includes a field-scale compositional reservoir flow model, an uncertainty quantification model and a neural network optimization process. The reservoir flow model has been constructed based on the field geophysical, geological, and engineering data. A laboratory fluid analysis was tuned to an equation of state and subsequently used to predict the thermodynamic minimum miscible pressure (MMP).A history match of primary and secondary recovery processes was conducted to estimate the reservoir and multiphase flow parameters as the baseline case for analyzing the effect of recycling produced gas, infill drilling and water alternating gas (WAG) cycles on oil recovery and CO2 storage. A multi-objective optimization model was defined for maximizing both oil recovery and CO2 storage. The uncertainty quantification model comprising the Latin Hypercube sampling, Monte Carlo simulation, and sensitivity analysis, was used to study the effects of uncertain variables on the defined objective functions. Uncertain variables such as bottom hole injection pressure, WAG cycle, injection and production group rates, and gas-oil ratio among others were selected. The most significant variables were selected as control variables to be used for the optimization process. A neural network optimization algorithm was utilized to optimize the objective function both with and without geological uncertainty. The vertical permeability anisotropy (Kv/Kh) was selected as one of the uncertain parameters in the optimization process.The simulation results were compared to a scenario baseline case that predicted CO2 storage of 74%. The results showed an improved approach for optimizing oil recovery and CO2 storage in the FWU. The optimization process predicted more than 94% of CO2 storage and most importantly about 28% of incremental oil recovery. The sensitivity analysis reduced the number of control variables to decrease computational time. A risk aversion factor was used to represent results at various confidence levels to assist management in the decision-making process. The defined objective functions were proved to be a robust approach to co-optimize oil recovery and CO2 storage. The Farnsworth CO2 project will serve as a benchmark for future CO2–EOR or CCUS projects in the Anadarko basin or geologically similar basins throughout the world.

Optimum design of CO2 storage and oil recovery under geological uncertainty

This paper presents an integrated numerical framework to co-optimize EOR and CO2 storage performance under uncertainty in the Farnsworth Unit (FWU) oil field in Ochiltree County, Texas. The framework includes a field-scale compositional reservoir multiphase flow model, an uncertainty quantification model and a neural network optimization process. The reservoir flow model has been constructed based on the field geophysical, geological, and engineering data. Equation of state parameters were tuned to achieve field measured fluid properties and subsequently used to predict the minimum miscible pressure (MMP).A history match of primary and secondary recovery processes was conducted to estimate the reservoir and multiphase flow parameters as the base case for analyzing the effect of recycling produced gas, infill drilling and water alternating gas (WAG) cycles on oil recovery and CO2 storage. A multi-objective optimization model was defined for maximizing both oil recovery and CO2 storage. The uncertainty quantification model comprising the Latin Hypercube sampling, Monte Carlo simulation, and sensitivity analysis, was used to study the effects of uncertain variables on the defined objective functions. Uncertain variables include bottom hole injection pressure, WAG cycle, injection and production group rates, and gas-oil ratio. The most significant variables were chosen as control variables to be used for the optimization process. A neural network optimization algorithm was utilized to optimize the objective function both with and without geological uncertainty. The vertical permeability anisotropy (Kv/Kh) was selected as one of the uncertain parameters in the optimization process.The simulation results were compared to a scenario baseline case that predicted CO2 storage of 74%. The results showed an improved approach for optimizing oil recovery and CO2 storage in the FWU. The optimization model predicted that about 94% of CO2 would be stored and most importantly, that this increased storage could result in about 25% of incremental oil recovery. The sensitivity analysis reduced the number of control variables to decrease computational time. A risk aversion factor was used to represent results at various confidence levels to assist management in the decision-making process. The defined objective functions were shown to be a robust approach to co-optimize oil recovery and CO2 storage.

Discrete Event Modeling and Simulation-Driven Engineering for the ATLAS Data Acquisition Network

The authors present an iterative and incremental development methodology for simulation models in network engineering projects. Driven by the DEVS (Discrete Event Systems Specification) formal framework for modeling and simulation, they aim to assist network design, test, analysis, and optimization processes. A practical application of the methodology is presented for a case study in the data acquisition system of the ATLAS particle physics experiment at CERN's Large Hadron Collider at CERN. By adopting the DEVS MaS formal framework in combination with software engineering best practices, the authors develop network simulation models along with enhanced modeling capabilities and boosted simulation performance for tools in a robust yet flexible way.

Properties of the Multiservice Erlang's Ideal Gradings

The design and optimization process of modern telecommunications networks is supported by a range of appropriate analytical models. A number of these models are based on the Erlang’s Ideal Grading (EIG) model, which is a particular case of non-full-availability groups. A possibility of the application of the EIG model results from the fact that telecommunications systems show properties and features distinctive to non-full-availability systems. No detailed studies that would decisively help determine appropriate conditions for the application of the EIG model for modeling of other non-full-availability groups, that would be models corresponding to real telecommunications systems, have been performed. Therefore, this article attempts to find an answer to the following question: what are the prerequisite conditions for the application of the EIG model and when the model can be reliably used?

Predictive Data Delivery to Mobile Users Through Mobility Learning in Wireless Sensor Networks

We consider applications, such as indoor navigation, evacuation, or targeted advertising, where mobile users equipped with a smartphone-class device require access to sensor network data measured in their proximity. Specifically, we focus on efficient communication protocols between static sensors and users with changing location. Our main contribution is to predict a set of possible future paths for each user and store data at sensor nodes with which the user is likely to associate. We use historical data of radio connectivity between users and static sensor nodes to predict the future user-node associations and propose a network optimization process, i.e., data stashing, which uses the predictions to minimize network and energy overheads of packet transmissions. We show that data stashing significantly decreases routing cost for delivering data from stationary sensor nodes to multiple mobile users compared with routing protocols where sensor nodes immediately deliver data to the last known association nodes of mobile users. We also show that the scheme provides better load balancing, avoiding collisions and consuming energy resources evenly throughout the network, leading to longer overall network lifetime. Finally, we demonstrate that even limited knowledge of the location of future users can lead to significant improvements in routing performance.

A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks

Today, tracking the growing interest in closed-loop supply chain shown by both practitioners and academia is easily possible. There are many factors, which transform closed-loop supply chain issues into a unique and vital subject in supply chain management, such as environmental legislation, customer awareness, and the economical motivations of the organizations. However, designing and planning a closed-loop supply chain is an NP-hard problem, which makes it difficult to achieve acceptable results in a reasonable time. In this paper, we try to cope with this problem by proposing a new and effective solution methodology. On the other hand, this research considers improving closed-loop supply chain network optimization processes through dealing with mathematical programming tools; developing a deterministic multi-product, multi-echelon, multi-period model; and finally presenting an appropriate methodology to solve various sizes of instances. Both design and planning decision variables (location and allocation) are considered in the proposed network. Besides, in order to have a reliable performance evaluation process, large-scale instances are regarded in computational analysis. Two popular meta-heuristic algorithms are considered to develop a new elevated hybrid algorithm: the genetic algorithm (GA) and particle swarm optimization (PSO). Analyzing the above-mentioned algorithms’ strengths and weaknesses leads us to attempt to improve the GA using some aspects of PSO. Therefore, a new hybrid algorithm is proposed and a complete validation process is undertaken using CPLEX and MATLAB software. In small instances, the global optimum points of CPLEX for the proposed hybrid algorithm are compared to genetic algorithm, and particle swarm optimization. Then, in small, mid, and large-size instances, performances of the proposed meta-heuristics are analyzed and evaluated. Finally, a case study involving an Iranian hospital furniture manufacturer is used to evaluate the proposed solution approach. The results reveal the superiority of the proposed hybrid algorithm when compared to the GA and PSO.

Analyzing the availability of TV white spaces in dynamic broadcast

TV White Spaces (TVWS) are a promising frequency resource which will potentially enable various new wireless applications in the field of consumer electronics. Typically, the availability of TVWS is calculated assuming a static frequency allocation of traditional digital terrestrial TV (DTT) networks. In the present paper, additionally, a flexible frequency allocation of a Dynamic Broadcast system is analyzed. A realistic case study is depicted, comparing a traditional DTT network and Dynamic Broadcast with respect to the available amount of TVWS. The outcome indicates that the freed capacity by implementing Dynamic Broadcast can result in a significant increase of available frequency resources for white space devices, as long as an appropriate allocation strategy is chosen. The differences of individual network configurations are studied by means of computer simulations and conclusions are drawn with regard to the network optimization process of Dynamic Broadcast.

Joint production and delivery lot sizing for a make-to-order producer–buyer supply chain with transportation cost

An integrated production/delivery quantity model in a make-to-order producer–buyer supply chain is analyzed in this paper. The coordinated policy is achieved by scheduling single setup at the producer with multi-delivery to the buyer. We develop two synchronized cost models that include setup cost at the producer, ordering cost at the buyer, inventory carrying cost, and transportation cost that is a fitted power function of delivery quantity, using actual shipping rate data. The mathematical analysis and computational study have demonstrated that significant cost savings can be realized by implementing the proposed approach into the network optimization process.

Development of a FDD-LTE scanning measurement system

This article introduces the development of a Frequency Division Duplexing Long Term Evolution (FDD-LTE) scanning measurement system which can quickly scan FDD-LTE cells and measure the quality of wireless signals. In this system development, a low complexity frame synchronization method is proposed for efficiently reducing the resource usage of the FPGA, and a co-channel cell detection scheme is also proposed and implemented. The success probability of this frame synchronization method is simulated and its effectiveness is verified with experimental testing. By using the co-channel cell detection scheme, co-channel cells can be correctly detected in practical measurement. Experimental testing results show that this FDD-LTE scanning measurement system can detect and measure a cell at a very high speed. The Physical Broadcast Channel (PBCH) decoding function is also included in this scanning measurement system and verified in field testing. Using this scanning measurement system, FDD-LTE system operators can launch the cell coverage measurement more conveniently and speed up the wireless network optimization process.

Introducing automated procedures in 3G network planning and optimization

Third generation (3G) networks have been launched for quite some time and cellular operators have already started to face the challenges when operating such kind of networks, compared to their 2G predecessors. In this paper, we give an overview of the network planning and optimization processes in 3G networks. Emphasis is given on the interdependence between coverage and capacity, which increases the complexity of operating 3G networks in a cost effective and quality assuring manner. We stretch out the importance of introducing automated procedures in these challenging tasks and describe main inputs and outputs of these procedures, such as initial network configuration and constraints, drive test data, and key performance indicators statistics and goals. We use two case studies from a operational 3G system to indicate the improvements on the network when these automated procedures are employed.

A novel fitting algorithm for Brillouin scattering spectrum of distributed sensing systems based on RBFN networks

In order to extract features for Brillouin scattering spectrum of distributed sensing systems with high accuracy, a novel fitting algorithm, using a hybrid algorithm based on Particle Swarm Optimization algorithm and Levenberg-Marquardt algorithm to optimize the optimization process of radial basis function networks, is explanatorily proposed. Compared the proposed algorithm with traditional BP neural networks, the five times polynomial curve and piecewise cubic spline interpolation in fitting the simulative and experimental spectrum, respectively, the evaluation parameter is relatively better than other three algorithms under the same experiment with different pulse widths. The numerical and experimental results showed that modified RBFN networks have some referential roles, which can guarantee the accurate measurement of Brillouin scattering spectrum.

Topographic optimal network design of the unified control points in Korea

In general, the geometric approach based on the criterion matrix has focused on the optimal network design. At the scale of the local area, however, the topographic undulations should be considered for ground applications, such as geoid determination. Since the multi-purpose unified control points (UCPs) are planned for providing gravimetric information as well as the three-dimensional positions, the effect of high-frequency signals from topography need to be considered in the network optimization process. In this study, an optimization procedure incorporating the geometric and topographic configuration is presented. A Digital Elevation Model (DEM) that represents terrain information is combined with the second-order design algorithm with Taylor-Karman structure. The smoothed DEM data are removed from the original dataset, resulting in high-frequency data only; the root mean squares of the residuals are computed to create the weight matrix. As a result, the directional pattern is clearly seen in the weight matrix, and the final location of the network shows the north-south directional properties. Once the network selection process is complete, the Minimum Spanning Tree (MST) is created to examine the distribution of the baselines. The statistics on the MST were used for the criterion of optimal network validation.

A New Training Approach for Parametric Modeling of Microwave Passive Components Using Combined Neural Networks and Transfer Functions

This paper presents a novel technique to develop combined neural network and transfer function models for parametric modeling of passive components. In this technique, the neural network is trained to map geometrical variables onto coefficients of transfer functions. A major advance is achieved in resolving the discontinuity problem of numerical solutions of the coefficients with respect to the geometrical variables. Minimum orders of transfer functions for different regions of geometrical parameter space are identified. Our investigations show that varied orders used for different regions result in the discontinuity of coefficients. The gaps between orders are bridged by a new order-changing module, which guarantees the continuity of coefficients and simultaneously maintains the modeling accuracy through a neural network optimization process. This technique is also expanded to include bilinear transfer functions. Once trained, the model provides accurate and fast prediction of the electromagnetic behavior of passive components with geometrical parameters as variables. Compared to conventional training methods, the proposed method allows better accuracy in challenging applications involving high-order transfer functions, wide frequency range, and large geometrical variations. Three examples including parametric modeling of slotted patch antennas, bandstop microstrip filters, and bandpass coupled-line filters are examined to demonstrate the validity of this technique.