Mobile Telecommunication Networks Research Articles

MHealth: A Sustainable Healthcare Model for Developing World

Health is a basic requirement to improve the quality of life. Providing effective health care is an essential component towards the social and economic development of a country. A large number of people in the developing countries, particularly in rural and remote areas, remained with no or little access to health care facilities. However, recent emergence of mobile communication technologies could play in improving healthcare services. There is a great potential in using mHealth as one of the supportive systems within the healthcare sector to solve the inequalities in healthcare delivery between rural and urban hospitals. This research aims to evaluate the potentialities, issues and challenges of developing mobile healthcare system in the developing world. We have proposed a potential mHealth model based on mobile telecommunication networks. This research offers a set of guidelines to aid the implementation of a successful mobile healthcare system.

Quality‐based charging solutions for wireless multimedia services

SUMMARYThe evolution of mobile telecommunication networks and access bandwidth has enabled operators to deliver IPTV services for mobile customers; however, the existing mobile charging solutions are not effectively addressing the billing requirements of multimedia streams. This paper develops a quality‐based charging solution for 3GPP wireless networks with the aim of providing a fair pricing solution for multimedia services. An appropriate business model is selected and a streaming proxy‐based charging architecture is developed based on the quality of the multimedia service. The proposed algorithms are theoretically evaluated by the derivation of the formulas of the distribution and the expected value of the price discount. A testbed is built and the findings are evaluated and compared with traditional charging functions. Copyright © 2014 John Wiley & Sons, Ltd.

Investment decisions in mobile telecommunications networks applying real options

This paper proposes a real options model for valuing the option to delay mobile telecommunications network investment decisions and a method for calculating the real options’ impact on mobile service costs. Ranges of option value multiples are calculated for the decisions to invest in a number of network elements, each representing a different part of the mobile network, subject to different demand and technological uncertainties. The value of the option to invest in each network element, net of future replacement options, is modeled as a function of the element’s total variable profit and the cost of investment in the element. The model and method are then applied to estimate the real options’ impact on mobile termination costs using real cost and volume data.

Visualization of trends in subscriber attributes of communities on mobile telecommunications networks

Churn, the decision for a subscriber to leave a provider, is frequently of interest in the telecommunications industry. Previous research provides evidence that social influence can be a factor in mobile telecommunications churn. In our work, presented at ASONAM, we presented a system, called ChurnVis, to visualize the evolution of mobile telecommunications churn and subscriber actions over time. First, we infer a social network from call detail records. Then, we compute components based on an overlay of this social network and churn activity. We compute summaries of the attributes associated with the subscribers and finally, we visualize the components in a privacy preserving way. The system is able to present summaries of thousands of churn components in graphs of hundreds of millions of edges. One of the drawbacks of the original approach was that churn components were sometimes very large, leading to over-aggregation in the summary data. In this extension of the ASONAM paper, we adapt the ChurnVis approach to operate on the output of a community finding algorithm and present new results based on this adaptation.

The Usage and Perceptions of Mobile Telecommunication Services by Selected South African Students

Young people are fascinated by new information and communication technologies. This is evident in the attraction to social networks and new modes of communication offered by mobile telecommunication networks. Beyond communicating with others, new generation mobile phones enable users to perform an assortment of tasks which were hitherto inconceivable. The youth use mobile technology to create and maintain social interactions while also relying on it for educational purposes. This paper, based on empirical research, examines the educational and social uses of mobile telecommunication services by first-year Information Technology (IT) students at the University of KwaZulu-Natal, South Africa. The study provides insight into the usage of mobile telecommunication services for different academic and social activities from students’ perceptions. The study identifies some factors that influence the adoption and usage of mobile phones in education. Students’ use of mobile telecommunications services is analyzed using the Technology Acceptance Model (TAM) and the theory of planned behaviour.

A Signal Strength Fluctuation Prediction Model Based on the Random Forest Algorithm

This article describes the effect of the weather on radio wave propagation in a mobile telecommunication network. The research is focused on urban and countryside environments where a correlation between the received signal power level and weather conditions is found using the Random Forest algorithm as a signal level approximator. The results achieved in this paper clearly indicate that it is possible to predict the behaviour of the received power level in relationship to atmospheric phenomena. DOI: http://dx.doi.org/10.5755/j01.eee.20.5.7112

Moodle Mobile Notifier

E-learning management systems (eLMS) are increasingly used as online communication platform between students and teachers in traditional, distance and open learning educational institutions. Moodle [10] is the most used open-source e-LMS platform with around 68 million users being served worldwide [9]. This serverside application can be accessed via a web browser, on any computer or Internet-enabled hand-held device (smart phone, tablet, etc.). However, this system has three limitations: first, the lack of synchronization between client and server applications, in other words between posting (server side) and viewing (client side) the information. The second limitation is that students cannot easily differentiate between newly and previously seen information. This is because most of the Moodle posts are not tagged with dates (except news and messages). The third limitation arises specifically when the Moodle server is accessed from a hand-held device using mobile telecommunication networks where bandwidth is considered as a scarce resource (low data rates and limited download/upload quotas). This limitation is caused by the ”useless” exchange of high amount of data to load all page contents even if no new information has been posted. The above listed limitations have negative impact on the tutor-student communication when eLMS platforms are used. This article proposes a system based on a three-tier architecture named Mobile Moodle Notifier (MMN) to overcome these limitations: the first tier is a mobile application (built on Google Android operating system [3]) that communicates with Moodle using low-level programming (socket programming) in order to reduce the bandwidth and download data consumption. The second tier is a server-side application that feeds the users (mobile application) with newly posted information from the third tier, which is the extended - Moodle database. Performance tests show a 50 times faster execution time and 300 times less download data consumption on average using MMN compared to accessing Moodle via a regular web browser.

Time-Delay Neural Network for Smart MIMO Channel Estimation in Downlink 4G-LTE-Advance System

Long-Term Evolution (LTE) is the next generation of current mobile telecommunication networks. LTE has a new flat radio-network architecture and significant increase in spectrum efficiency. In this paper, main focus on throughput performance analysis of robust MIMO channel estimators for Downlink Long Term Evolution-Advance (DL LTE-A)-4G system using three Artificial Neural Networks: Feed-forward neural network (FFNN), Cascade-forward neural network (CFNN) and Time-Delay neural network (TDNN) are adopted to train the constructed neural networks‟ models separately using Back-Propagation Algorithm. The methods use the information received by the received reference symbols to estimate the total frequency response of the channel in two important phases. In the first phase, the proposed ANN based method learns to adapt to the channel variations, and in the second phase, it estimates the MIMO channel matrix and try to improve throughput of LTE. The performance of the estimation methods is evaluated by simulations in Vienna LTE-A DL Link Level Simulator. Performance of the proposed channel estimator, Time-Delay neural network (TDNN) is compared with traditional Least Square (LS) algorithm and ANN based other estimators for Closed Loop Spatial Multiplexing (CLSM) Single User Multi-input Multi-output (MIMO-2×2 and 4×4) in terms of throughput. Simulation result shows TDNN gives better performance than other ANN based estimations methods and LS.

Population Mobility Dynamics Estimated from Mobile Telephony Data

In the last decade, mobile phones and mobile devices using mobile cellular telecommunication network connections have become ubiquitous. In several developed countries, the penetration of such devices has surpassed 100 percent. They facilitate communication and access to large quantities of data without the requirement of a fixed location or connection. Assuming mobile phones usually are in close proximity with the user, their cellular activities and locations are indicative of the user's activities and movements. As such, those cellular devices may be considered as a large scale distributed human activity sensing platform. This paper uses mobile operator telephony data to visualize the regional flows of people across the Republic of Ireland. In addition, the use of modified Markov chains for the ranking of significant regions of interest to mobile subscribers is investigated. Methodology is then presented which demonstrates how the ranking of significant regions of interest may be used to estimate national population, results of which are found to have strong correlation with census data.

Artificial Neural Network trained by Genetic Algorithm for Smart MIMO Channel Estimation for Downlink LTE-Advance System

Long-Term Evolution (LTE) is the next generation of current mobile telecommunication networks. LTE has a flat radio-network architecture and significant increase in spectrum efficiency, throughput and user capacity. In this paper, performance analysis of robust channel estimators for Downlink Long Term Evolution-Advanced (DL LTE-A) system using three Artificial Neural Networks: Feed-forward neural network (FFNN), Cascade-forward neural network (CFNN) and Layered Recurrent Neural Network (LRN) are trained separately using Back-Propagation Algorithm and also ANN is trained by Genetic Algorithm (GA). The methods use the information got by the received reference symbols to estimate the total frequency response of the channel in two important phases. In the first phase, the proposed ANN based method learns to adapt to the channel variations, and in the second phase it estimates the channel matrix to improve performance of LTE. The performance of the estimation methods is evaluated by simulations in Vienna LTE-A DL Link Level Simulator in MATLAB software. Performance of the proposed channel estimator, ANN trained by Genetic Algorithm (ANN- GA) is compared with traditional Least Square (LS) algorithm and ANN based other estimator like Feed- forward neural network, Layered Recurrent Neural Network and Cascade-forward neural network for Closed Loop Spatial Multiplexing (CLSM)-Single User Multi-input Multi-output (MIMO-2×2 and 4×4) in terms of throughput. Simulation result shows proposed ANN- GA gives better performance than other ANN based estimations methods and LS. Index Terms — LTE-A, MIMO, Artificial Neural Network (ANN), Back-Propagation, Layered Recurrent Neural Network (LRN), Feed-forward neural network (FFNN), Cascade-forward neural network (CFNN), Genetic Algorithm (GA).

Wining the Talent War via Effective Employee Engagement: A Case Study

As the corporate sector entrenches itself among the players of the global economy, more sophisticated strategies and novel business trends are required so as to enhance profitability and lower business costs. It is against this backdrop that the researchers undertook this study to explore possible alternatives of addressing the talent war rather than the orthodox methods. A multinational telecommunication company located in Ghana (Mobile Telecommunication Network, MTN Ghana) was selected as a case, due to its competitiveness and world presence, especially for its dominance in Africa. With substantive review of the literature, five thematic effective engagement variables were discovered: the work environment, supervisor-employee relationship, job satisfaction, HRM Practices, and organization culture. A quantitative approach was adopted as the main tool to gather 137 valid responses from employees of MTN Ghana. Some comprehensive interviews were also conducted to saturate the robustness of the findings. Therefore, the methodological approach to this research outcome could be best described as triangulation. Interestingly, the findings were insightful, intriguing and competitively viable. The role of human capital in the success of every giant firm certainly could not be overemphasized. Obviously, our findings shed amazing light on why the company is beating its industry competitors like Tigo and Vodafone hands down. The company is committed to her employees and the inverse is also true-commitment begets engagement. Though, few gaps were identified, on a whole the talent war is a done deal when the engagement strategies are sound.

Time dependence of environmental electric field measurements and analysis of cellular base stations

It is well-known that the rapid growth of the mobile telecommunication networks all over the world has raised the concern about the adverse health effects. Today some authorities and universities collaborate together for the measurements of the Electromagnetic Fields (EMF) at the locations close to the base stations. Continuous monitoring of EMF intensity at living spaces is vital to ensure its level complies with the limits set by international authorities. Measurement of EMF intensity in a medium can be performed either sequentially by a moveable agent or continuously by a single point agent. In this paper, the single point method is adopted to track the time-dependent characteristics of the electric field of a base station. It is found that electromagnetic fields generated by the base station change according to the mobile user traffic, which is time-dependent. EMF levels around the base stations are measured on a daily, weekly and monthly basis, and comparisons are compared with the limits set by the International Commission on Non Ionizing Radiation Protection (ICNI RP). It is observed that maximum and minimum electric fields are found to be E max =2.36 V/m and E min = 1.18 V/m, respectively.

Optimizing the Cellular Network Planning Process for In-Building Coverage using Simulation

The global scenario of mobile telecommunications networks suggests that the next years the number of mobile users will continue growing as well as the services demand. The biggest growth is expected mainly in Asiatic and developing countries. Particularly, the Mexican telecommunications market suggests that if the number of mobile users will continue growing in Mexico as in the last years, then the mobile networks operators should increment the network capacity and the in-building coverage in order to maximize the quality of service. Also, network operators should offer attractive plans to users in order to maximize their economic benefits. Therefore, this research study aims to develop a simulation model to analyze the services transmission in a Third Generation mobile network and maximize the network coverage and the theoretical economic benefit of network operators. The delivered services included are voice, data and video. Using this simulation model we obtained the optimal cost-effective network design in a scenario of a Mexican airport. This design consisted of six cells, each one configured with an Omni-directional antenna and capacity of seven traffic channels. Through the traffic channels were delivered the services: sound, multimedia, narrow band and wide band, with a data rate of 12 kb/s, 128kb/s, 384 kb/s, and 1920 kb/s, respectively. The maximum economic benefit was 46.6% using the optimal network design. We propose this simulation model as a tool for decision-making support of mobile network operators in Mexico.

Study of ANN Configuration on Performance of Smart MIMO Channel Estimation for Downlink LTE-Advanced System

Long-Term Evolution (LTE) is the next generation of current mobile telecommunication networks. LTE has a new flat radio-network architecture and significant increase in spectrum efficiency. In this paper, performance analysis of robust channel estimators for Downlink Long Term Evolution- Advanced (DL LTE-A) system using three Artificial Neural Network ANN Architectures: Feed-forward neural network, Cascade-forward neural network and Layered Recurrent Neural Network (LRN) are adopted to train the constructed ANNs models separately using Back-Propagation Algorithm. The methods use the information got by the received reference symbols to estimate the total frequency response of the channel in two important phases. In the first phase, the proposed ANN based method learns to adapt to the channel variations, and in the second phase it estimates the channel matrix to improve performance of LTE. The performance of the estimation methods is evaluated by simulations in Vienna LTE-A DL Link Level Simulator. Performance of the proposed channel estimator, Layered Recurrent Neural Network is compared with traditional Least Square (LS) algorithm and ANN based other estimator like Feed-forward neural network and Cascade-forward neural network for Closed Loop Spatial Multiplexing-Single User Multi-input Multi- output (2×2 and 4×4) (CLSM-SUMIMO) in terms of throughput. Simulation result shows LRN gives better performance than other ANN based estimations methods and LS.

Deriving the Vehicle Speeds from a Mobile Telecommunications Network

Vehicle speeds are often measured by intelligent transportation systems (ITSs) by utilizing sensors or software solutions. Our previous work proposed the Lin-Chang-Huangfu (LCH) scheme to compute the cell residence times by the standard counter values in mobile telecommunications switches. In this paper, we use mathematical and statistical developments to investigate the accuracy of the LCH scheme by deriving the bias of the cell residence times computed in this scheme. Then, we extend the LCH scheme with some filtering and compensation techniques for vehicle speed estimation and validate our approach with vehicle detector (VD) measurements at National Highway 3, Longtan Township, Taoyuan County, Taiwan. Our study indicates that the LCH scheme is an effective approach to vehicle speed estimation.

PAPR Reduction for 3FPP LTE OFDMA System

LTE (long term evolution) is come to meet our desired high speed data communication.LTE is the last step toward the 4th generation (4G) of radio technologies designed to increase the capacity and speed of mobile telephone networks. Where the current generation of mobile telecommunication networks are collectively known as 3G (for third generation), LTE is marketed as 4G. LTE uses Orthogonal Frequency Division Multiplexing (OFDM) for the downlink - that is, from the base station to the terminal. OFDMA meets the LTE requirement for spectrum flexibility and enables cost-efficient solutions for very wide carriers with high peak rates. However, one of the main drawbacks of the OFDMA modulation technique is the large peak-to-average power ratio (PAPR) of the transmitting signals. This high PAPR causes interference when the OFDM signals are passed through an amplifier which does not have enough linear range. Several PAPR reduction techniques such as magnitude clipping, block coding, and partial transmit sequence (PTS) and pulse shaping have been proposed to reduce the PAPR. In this paper, we evaluate a clipping based PAPR reduction technique. The complementary cumulative distribution function (CCDF) of the PAPR, which can be used to evaluate the PAPR reduction performance, is derived for OFDMA signals.

Mobile telecommunication networks choice among Ghanaians

The paper investigates the factors influencing customers choice of telecommunication network in Ghana. The survey design was employed to enable the researchers perform statistical analysis. Questionnaire consisting of Likert scale question was used to collect the primary data. Multiple regression analysis was performed to ascertain the factors influencing customers’ choice of telecommunication networks. The study found six factors that influence customers to choose a particular network. These factors include; brand awareness, brand image, perceived quality, price, convenience and brand loyalty. The study concludes that all the six factors contribute to the factors that drive consumer choice of telecommunications service in Ghana.

Securitatea comunicațiilor în cea mai recentă generație de rețele mobile - LTE

Securitatea comunicațiilor în cea mai recentă generație de rețele mobile - LTE

Predicting Human Movement Based on Telecom's Handoff in Mobile Networks

Investigating human movement behavior is important for studying issues such as prediction of vehicle traffic and spread of contagious diseases. Since mobile telecom network can efficiently monitor the movement of mobile users, the telecom's mobility management is an ideal mechanism for studying human movement issues. The problem can be abstracted as follows: What is the probability that a person at location A will move to location B after T hours. The answer cannot be directly obtained because commercial telecom networks do not exactly trace the movement history of every mobile user. In this paper, we show how to use the standard outputs (handover rates, call arrival rates, call holding time, and call traffic) measured in a mobile telecom network to derive the answer for this problem.

Evaluating the investment decision-making process for business expansion into Africa: A case study

Africa is a potential domain for international business. However, numerous uncertainties characterize this environment and the challenge for multinationals remains the ability to assess the true value of an Africa-bound investment project. A telecommunications’ survey was conducted on Siemens Southern Africa (Siemens) and Mobile Telecommunications’ Network (MTN) and the following observations were made: (1) Approaches used by the businesses to value Africa-bound investments were not comprehensive and inclusive. (2) Neutrality existed to the suggestion that Africa is unique and that investment decisions should be customized to suit it. (3) Certain approaches used by the businesses were modified to suit pertaining investment circumstances thereby differing from literature, and (4), participants desired to learn new ways of improving this process suggesting dissatisfaction with the current norm. This paper presents the conflicting ideologies about the decision-making process forbusiness expansion into Africa and suggests ways of improving the process.