Communication Network Planning Research Articles

Complexity of the multiobjective minimum weight minimum stretch spanner problem

AbstractIn this paper, we take an in-depth look at the complexity of a hitherto unexplored multiobjective minimum weight minimum stretch spanner problem; or in short multiobjective spanner (MSp) problem. The MSp is a multiobjective generalization of the well-studied minimum t-spanner problem. This multiobjective approach allows to find solutions that offer a viable compromise between cost and utility—a property that is usually neglected in singleobjective optimization. Thus, the MSp can be a powerful modeling tool when it comes to, e.g., the planning of transportation or communication networks. This holds especially in disaster management, where both responsiveness and practicality are crucial. We show that for degree-3 bounded outerplanar instances the MSp is intractable and computing the non-dominated set is BUCO-hard. Additionally, we prove that if $${\textbf{P}} \ne \textbf{NP}$$ P ≠ NP , the set of extreme points cannot be computed in output-polynomial time, for instances with unit costs and arbitrary graphs. Furthermore, we consider the directed versions of the cases above.

Developing an Intelligent Decision Support System for large-scale smart grid communication network planning

The placement of routers and gateways in wireless communication networks for Smart Grid Advanced Metering Infrastructure (AMI) is a complex problem often addressed in the literature through heuristics, clustering, or linear programming approaches. In large-scale scenarios, the complexity is directly related to the region’s number of smart meters and poles. The terrain profile can introduce signal quality degradation between a smart meter and the routers and gateways, further complicating the problem. This study presents the AIDA-ML method, representing a preliminary step toward developing an Intelligent Decision Support System (IDSS) for an effective positioning strategy. AIDA-ML incorporates a feature engineering-based strategy and utilizes machine learning algorithms to learn from the results computed by a heuristic method called AIDA. The heuristic approach is too time-consuming, relying on external resources like numerous terrain profile API calls and a deep understanding of wireless communication loss models. To implement a machine learning-based and more straightforward approach, a feature engineering process is employed to capture the operational characteristics and results of the analytical method while requiring less processing time. Through experiments conducted with real-world data from 26 cities in the state of Paraná, Brazil, comprising 466,237 smart meters and 352,867 poles, the results obtained using machine learning algorithms suggest that AIDA-ML can ensure the connection coverage of smart meters while meeting the same minimum requirements established for the analytical method. Moreover, AIDA-ML offers the advantage of reducing the processing time by 87.60% and by 96.86% the overall search space size compared to the heuristic approach.

Exploring the Application of Design Thinking Methodology in Cellular Communications Network Planning and Deployment

Cellular network planning and deployment represent a set of activities that operators have been performing since the advent of mobile wireless communication systems. Decisions along this route traditionally have been based on engineering or commercial criteria, focusing on providing the best service to users. Although operator companies gather information regarding perceived quality of service among their subscribers, mainly using surveys, engineers work with numerical data more than with people’s interests, feelings, perceptions, or hopes. In other sectors, Design Thinking has arisen as a methodology that allows designers to involve real-world users in implementing new products or services. Thus, we are introducing this methodology for cellular networking, exploring its application in a process that could be designed in a more human-centered way. With this aim, we analyzed the different steps that could be included in the network planning and deployment. From this analysis, we detected which actions have chances to be explored in a human-centered view and thus, can be improved with some Design Thinking tips. Finally, we developed an experience of limited potential, comparing the insights obtained by an empathic interview to those given by traditional surveys, to show what Design Thinking could provide.

ANALISIS PERBANDINGAN LINK BUDGET UNTUK MENGHITUNG PATH LOSS PADA DAERAH URBAN DAN SUBURBAN MENGGUNAKAN METODE OKUMURA HATTA

As time goes by, the development of cellular communication technology is increasing, this is related to the increasing need for cellular communication. Cellular communication propagation media uses air, propagation between sender and receiver can cause propagation losses and affect the quality of communication. Therefore we need a communication network planning. In wireless communication planning requires link budget calculation. The link budget calculation is useful for cost efficiency and building a quality communication system. Therefore, in this study, link budget calculations in urban and sub-urban areas were made using the Okumara Hatta method and simulations using Matlab software. In this study using data from various sources. The data is used to calculate the path loss value and the received power level. From the results of research using the Okumara-Hatta method, it proves that the greater the distance or distance of the MS (mobile station) to the BTS (Base Transceiver Station), the greater the path loss experienced. The carrier frequency, BTS height, and MS height also affect the starting point of the path loss.

Accurate Base Station Placement in 4G LTE Networks Using Multiobjective Genetic Algorithm Optimization

Cellular mobile communication network planning and optimization involve a complex engineering process that deals with network fundamentals, radio resource elements, and critical decision variables. The continuous evolution of radio access technologies provides new challenges that necessitate efficient radio planning and optimization. Therefore, the planning and optimization algorithms should be highly efficient, advanced, and robust. An important component of 4G LTE network planning is the proper placement of evolved node base stations (eNodeBs) and the configuration of their antenna elements. This contribution proposes a multiobjective genetic algorithm that integrates network coverage, capacity, and power consumption for optimal eNodeB placement in an operational 4G LTE network. The multi-objective-based genetic algorithm optimization has been achieved using the optimization toolbox in MATLAB. By leveraging the proposed method, the effect of different population sizes on the cost of placing the eNodeBs and the percentage coverage of the eNodeBs in a given cell is determined. As a result, the optimal selection technique that minimizes the total network cost without compromising the desired coverage and capacity benchmarks is achieved. The proposed automatic eNodeB antenna placement method can be explored to optimize 4G LTE cellular network planning in related wireless propagation environments.

Novel Path Counting-Based Method for Fractal Dimension Estimation of the Ultra-Dense Networks

Next-generation networks, including the Internet of Things (IoT), fifth-generation cellular systems (5G), and sixth-generation cellular systems (6G), suffer from the dramatic increase of the number of deployed devices. This puts high constraints and challenges on the design of such networks. Structural changing of the network is one of such challenges that affect the network performance, including the required quality of service (QoS). The fractal dimension (FD) is considered one of the main indicators used to represent the structure of the communication network. To this end, this work analyzes the FD of the network and its use for telecommunication networks investigation and planning. The cluster growing method for assessing the FD is introduced and analyzed. The article proposes a novel method for estimating the FD of a communication network, based on assessing the network’s connectivity, by searching for the shortest routes. Unlike the cluster growing method, the proposed method does not require multiple iterations, which reduces the number of calculations, and increases the stability of the results obtained. Thus, the proposed method requires less computational cost than the cluster growing method and achieves higher stability. The method is quite simple to implement and can be used in the tasks of research and planning of modern and promising communication networks. The developed method is evaluated for two different network structures and compared with the cluster growing method. Results validate the developed method.

Extremely Randomized Trees Regressor Scheme for Mobile Network Coverage Prediction and REM Construction

In mobile communications network planning (and designing any radio system), coverage prediction helps network operators optimize cellular networks to improve customer experience. Accordingly, several path-loss models have been proposed that depend on many conditions, such as suitable selection of the terrain for each model, the height of the receiver and transmitter above ground and the distance between them, and the presence of obstacles. This may increase the prediction error between actual and estimated values, which change according to the propagation model selected. To overcome these problems, we propose a novel approach to mobile coverage prediction based on an extremely randomized trees regressor (ERTR) algorithm. In addition, we construct a radio environment map (REM) over a Google Earth digital map to improve visualization of the results and to easily detect coverage holes and traffic hotspots. For this purpose, we utilize a dataset with real measurements collected from Victoria Island and Ikoyi in Lagos, Nigeria. For performance evaluation, we use k-fold cross-validation based on four error metrics: relative error, root mean squared error, mean absolute error, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score. The proposed ERTR scheme achieves the best performance in terms of accuracy and computational load in predicting the reference signal received power and the received signal strength indicator value. We prove this with extensive simulation analysis and by comparing the error metrics of the proposed ERTR approach with an existing method widely used to perform coverage prediction, called ordinary kriging. We also compared seven machine learning regression algorithms, namely, random forest, a bagging regressor, support vector regression, k-nearest neighbors, a deep neural network, Gaussian process regression, and the decision tree.

Heterogeneous communication network planning based on ant colony algorithm in smart grid

With the development of smart grids, highly automated power systems increasingly rely on communication networks, and research on the reliability, safety, and economy of heterogeneous communication networks is particularly important. Traditional network planning methods do not take into account network load problems, resulting in serious network congestion and long delays. Aiming at the above problems, this paper proposes a heterogeneous communication network planning method for a smart grid based on an improved ant colony algorithm. This method improves the original ant colony algorithm by updating the pheromone concentration of the network node in real time and realizes the planning of heterogeneous communication networks in the smart grid. Simulation results show that the proposed method has the advantages of avoiding local network congestion, reducing the average delay, and improving network stability.

Optimal topology planning of electromagnetic waves communication network for underwater sensors using multi-objective optimization algorithms (MOOAs)

“Extremely High Frequency (EHF)” and “Very high frequency (VHF)” bands are mainly utilized with “Underwater Wireless Sensor Networks (UWSNs)” for communication purposes. However, due to the mobility of underwater sensors in water because of the water tide, the EHF/VHF signals may attenuate, lose or fade depending on the condition of the water. Therefore, it is a challenging stint of finding the optimal parameters of UWSN topology planning. In this paper, three “Multi-Objective Optimization Algorithms (MOOAs)” have been utilized to mitigate this problem, namely MOSFP, SPEA2 and NSGA-II. This work also intends to minimize path loss. On the other hand, it intends to maximize the power density of the network. Various network configurations, such as distance between sender and receiver, water conductivity and water permeability, are considered to evaluate the proposed objective models. Qualitative and quantitative tests have been conducted to analyze the results. From the analysis of the intersection point of Pareto-front of the objective functions, it is shown that all the algorithms find the optimal distance between transmitter and receiver, which balances the aforementioned maximization and minimization objective functions. This value is 36 m.

ANN-based model for multiband path loss prediction in built-up environments

Path loss propagation models are critically needed for optimum planning and deployment of wireless communication networks. However, the complexity exhibited by the propagated signals makes the prediction of the received losses difficult in built-up environments. There is however a new paradigm shift towards the application of computational methodologies, such as the Artificial Neural Networks (ANN), for multi-band path loss prediction. In this paper, we have developed a new ANN-based model for path loss prediction. The model was developed using large scale path loss data collected across 485 base stations in 6 urban cities of Nigeria, West Africa. The data collection, which spanned a period of 9 years, were taken over open areas, sub urban and urban environments, and the bands considered were 89.3 MHz, 103.5 MHz, 203.25MHz, 429.25 MHz, 529.25MHz, 615.25MHz, 629.25MHz, 900MHz, 1800 MHz and 2100MHz. The developed model was validated using independent path loss data across different frequencies, environments; and, distances and the results were compared with the popular empirical models such as Hata, COST 231 and Egli models, and to previously published ANN-based multi-frequency models. The global performance of 4.81 dB was obtained in terms of RMSE value with an R-value of 0.96, thus outperforming the existing ANN-based path loss models that were developed for multiple frequencies. Based on these findings, the proposed model can be deployed across all categories since the average RMSE values are all within the acceptable thresholds. Furthermore, the model is multi-frequency, thus will be suitable for multiple and complex environments, and usable for both short- and long-range wireless communication networks

Terrain-based adaption of propagation model loss parameters using non-linear square regression

Reliable and real-time propagation loss modeling play a significant role in the efficient planning, development, and optimization of macrocellular communication networks in a given terrain. Thus, the need to adapt or tune an existing model to enhance its signal prediction accuracy in a specified terrain becomes imperative. In this paper, we proposed and applied a non-linear square regression method based on the Levenberg-Marquart (LM) algorithm to adapt and improve the empirical propagation loss estimation accuracy of the Egli model for two major cities in Nigeria. A comprehensive propagation loss measurement acquired over Long Term Evolution (LTE) mobile broadband networks operating at 2630 MHz for four different cities was collected using TEMS investigation tools to achieve the Egli model adaption. Results indicate that the adapted Egli model displays a high estimation accuracy over the Gauss-Newton (GN) algorithm leveraging the non-linear regression method employed to benchmark the propagation loss estimation. Using six standard statistical indicators, the adapted Egli model displayed lower estimation errors than the classical Egli model across the tested locations in the two cities investigated. Finally, the LM-adapted Egli model was compared with extensive measurements from another eNodeB in Port Harcourt different from the initial four eNodeBs investigated. The results indicate that the adapted model is suitable for deployment in related macrocellular environments.

Research on Secure Communication Network Planning and Design

Research on Secure Communication Network Planning and Design

Optimization of Backbone Optical Network for Power Communication Based on Critical Link

The existing optimization algorithms for power communication backbone optical networks do not comprehensively consider the real-time performance of power communication services and the difference of substation voltage levels, and the optimization results are difficult to meet the actual needs of power communication networks. Firstly, this paper uses the link critical comprehensive index to express the characteristics of critical links, and designs a critical link identification algorithm; then, on the basis of link critical ranking, the network performance is optimized by using link redundancy and link expansion methods; finally, the feasibility and correctness of the method are verified by an example of provincial power backbone optical network. The research results have reference value for the planning, design and operation management of power communication network.

Ultra-low latency networks and bridging the digital divide between Russian Federation regions

Research subject. The article is devoted to the analysis of the &#x0D; apabilities of telecommunication networks with ultra low latency to reduce the digital divide between the regions of the Russian Federation. Method. Systemic and technical and economic analysis. Core results. Determination of communication networks as the basis for reducing the digital divide, establishing the relationship between the Gross Regional Product and the share of employees in the field of informatization and communications, typing of the territories of the Russian Federation according to the possibilities of introducing communication network services with ultra low delays. Practical relevance. The results of the article can be used in the implementation of the digital economy program, scientific and design organizations in the planning and design of communication networks, as well as by universities in the educational process.

Research on Power Communication Network Planning Based on Information Transmission Reachability Against Cyber-Attacks

The deep integration of computing, communication, and information technologies not only improves the operation level of the power grid, but also brings the threat of cyber-attacks on the power grid. This article studies power communication network topology planning problem considering both the normal operation level and the ability to against cyber-attacks from the perspective of information transmission reachability. First, by analyzing the game relationship between ensuring the normal operation level and defending against cyber-attacks, a framework of power communication network topology planning based on game theory is proposed. In this framework, the traditional planner tries to maximize the information transmission reachability of normal operation, while the anti-cyber-attack planner wants to minimize the information transmission reachability of cyber-attacks. Second, considering whether there are uncertainties in the planning, a deterministic topology planning model is proposed for the traditional planner, and an uncertainty topology planning model is proposed for the anti-cyber-attack planner. Third, considering the competition between the two planners, an improved particle swarm algorithm is introduced to solve the models. Finally, a standard example and a practical power communication network are used to verify the correction and effectiveness of the method.

Research on Cellular Wireless Location Algorithm Considering TDOA Location Technology under the Background of Network Big Data Fusion

The background and foundation of applying big data analysis to mobile communication network planning is precisely the current rapid growth of network data and the update and development of big data technology. With the help of big data analysis tools, based on the current communication network, planning goals, it can be more comprehensive Analyze and explore the value perception and behavior trajectory of network users, etc., so as to determine the market demand trend, formulate a more scientific and reasonable planning program. In short, the purpose of the application of big data in communication network, planning is to promote the healthy development of the network. The cellular wireless positioning algorithm that realizes TDOA positioning technology by using big data is a relatively important technology, which is studied in this paper.

Analysis of Joint Channel Coexistence Time at Space-Diversity Radio Reception of Meteor Reflections

This paper examines the possibility of joint reception of radio reflections from the same meteor trails simultaneously at two spaced receivers. A refined simulation of a joint reception area (JRA) of meteor radio reflections on the basis of a diffraction theory of oblique scattering of radio waves off ionized meteor trails is performed for radio links of moderate (500 km) and medium (1000 km) lengths. The obtained results may be particularly useful for territory planning of meteor burst communication (MBC) networks with the spatial division of subscribers (SDMA technology). The simulated JRAs also characterize an interception probability of messages. This knowledge is especially important in designing highly secure MBC, such as meteor key distribution systems, intended for the creation of two identical copies of a shared secret key at both sides of the meteor radio link.

Power Communication Network Design Considering Global Information Fusion Part One Overall Architecture

This paper reviews the development of power communication network, followed by the situation and demand that power communication network is facing. Aiming at the problems, such as lack of global planning platform for power optical transmission network, lack of precise planning for power wireless private network and lack of whole process connection for planning information, the planning forecasting strategy and planning effectiveness evaluation index system based on business priority are put forward. The whole process information management system structure is constructed, which is composed of the whole process information organization module, the technology module and the collaboration module, etc. It will cover three major stages of power communication network in an all-round way (communication network planning, construction and post-evaluation stage), and form the "headquarters-provinces-municipalities-counties" four-level linkage scientifically.

Electric power communication bandwidth prediction based on adaptive extreme learning machine

Bandwidth demand forecasting is the basis and foundation of the power communication network planning. For the traditional neural network learning, there are many problems, such as slow convergence speed, more iterative times, and easy to fall into local optimum. An adaptive extreme learning machine model based on the theory of extreme learning machine and K nearest neighbour theory is proposed to predict the bandwidth of electric power communication. The adaptive metrics of inputs can solve the problems of amplitude changing and trend determination, and reduce the effect of the over-fitting of networks. The proposed algorithms are validated using real data of a province in China. The results show that this method is better than the traditional neural network, autoregressive models, self organisation models, and single extreme learning machine model. It can be used in electric power communication bandwidth prediction.

City-hubs for smarter cities. The case of Lille \u201cEuraFlandres\u201d interchange

City-hubs for smarter cities. The case of Lille \u201cEuraFlandres\u201d interchange