Cellular Operators Research Articles

Carbon-Neutral Cellular Network Operation Based on Deep Reinforcement Learning

With the exponential growth of traffic demand, ultra-dense networks have been proposed to cope with such demand. However, the increase of the network density causes more power use, and carbon neutrality becomes an important concept to decrease the emission and production of carbon. In cellular networks, emission and production can be directly related to power consumption. In this paper, we aim to achieve carbon neutrality, as well as maximize network capacity with given power constraints. We assume that base stations have their own renewable energy sources to generate power. For carbon neutrality, we control the power consumption for base stations by adjusting the transmission power and switching off base stations to balance the generated power. Given such power constraints, our goal is to maximize the network capacity or the rate achievable for the users. To this end, we carefully design the objective function and then propose an efficient Deep Deterministic Policy Gradient (DDPG) algorithm to maximize the objective. A simulation is conducted to validate the benefits of the proposed method. Extensive simulations show that the proposed method can achieve carbon neutrality and provide a better rate than other baseline schemes. Specifically, up to a 63% gain in the reward value was observed in the DDPG algorithm compared to other baseline schemes.

A value-added IoT service for cellular networks using federated learning

The number of Internet-of-Things (IoT) devices is expected to reach 64 billion by 2025. These IoT devices will mostly use cellular networks for transferring a huge amount of IoT data to the cloud for machine learning (ML) based forecasting. Keeping in view a large number of application scenarios for highly resource constraint IoT devices connected with the cellular networks, we propose a value-added IoT service (VAIS) for the cellular network operators based on the federated learning (FL) paradigm. Through simulation experiments, we show, for real air quality data and specific ML models, the proposed VAIS reduces the backhaul data by 70× and requires less energy than its equivalent cloud-based conventional approach, however, with a slight increase in communication time. From the insights we gained, we believe that a properly designed VAIS would efficiently utilize network resources, not only reduce management for IoT users but also the operating costs for cellular operators, and encourage IoT applications on limited backhaul cellular networks.

A 5G New Radio-Based Terrestrial Broadcast Mode: System Design and Field Trial

Terrestrial broadcast has traditionally targeted TV receivers in the home. But today’s most popular screens are those of SmartPhones and Tablet PCs. (Painful) Experience has shown that in order to reach those gadgets, IEEE- and 3GPP-specified solutions are required. Starting with TOoL+, the authors have proposed bridges between traditional broadcast and today’s wireless connectivity. Within the set of specifications of 3GPP such bridges named MBS, MBMS, and FeMBMS now exist – with limited acceptance by cellular network operators so far. The rapidly growing number of popular data services to SmartPhones, connected cars, etc., and 5G New Radio may change this in the future. Therefore, this paper presents a broadcast extension of today’s 5G New Radio supporting the implementation of High Tower High Power/Medium Power Medium Tower networks overlaying cellular networks. The extension was implemented using software defined radio and tested in a real-world field trial in Germany.

Method for QOE monitoring and increasing in cellular networks based on QOE-to-QOS mapping using spline approximation

The current level of development of the cellular services market, qualitative changes in the means and methods of providing services, and the increased volume and diversity of information circulating in cellular networks have evoked the need for service quality assessment system improvement. To maintain competitiveness, the main efforts of operators are aimed at improving quality and increasing the service life of subscribers in the network by ensuring the required level of customer satisfaction in high-quality services through a system of organizational–technical and socioeconomic measures to bring the achieved level of quality-of-service (QoS) provision in accordance with the existing, emerging, or projected needs of subscribers. To achieve improved QoS provision, a functional relationship between network parameters has been established: The impact of key performance indicators on a key quality indicator through the use of cubic Hermitian splines (CHS) has been determined. The use of splines, as a signal model, can significantly improve the quality of signal processing due to the continuity of values and partial derivatives in the joints of spline gluing. CHS are characterized by calculation simplicity, as they provide high-speed computing, which, in turn, is important for real-time work when processing large data sets. Experimentally, it was shown that the use of splines allows for the ability to calculate statistical estimates of the required parameters of spline approximations, but also their confidence intervals, which increases the accuracy and probability of further calculations and is a distinct advantage of this approach. Also, the methods of service quality management through machine learning have been improved, which are effective tools for modeling integrated indicators of communication services quality control, monitoring their condition in terms of final copies of services, determining the causes of degradation, and reporting. It monitors the service performance provided by the cellular operator. Finally, the method checks the readiness and availability of services, detects network nodes through which quality degradation occurs, collects various quality metrics, and compares them with pre-installed quality indicators.

INTELLIGENT SYSTEM FOR AUTOMATION SEARCH OF PUBLIC TRANSPORT ROUTES

This article is devoted to automation of search of routes of passenger flows of inhabitants of the city on the basis of routes of public transport. To do this, it is proposed to use satellite data that reflect the movement of passengers. Based on the analysis of data arrays of information on the movement of cellular subscribers and the use of message messages to determine the effectiveness of passenger routes of public transport, the article proposes to create a system of automatic search of public transport routes, which includes different classes of charts and data bases. You can use data from cellular operators to do this. This can increase the accuracy of certain traffic volumes and the ability to monitor them in real time. Reliable information on the routes of urban public transport allows to determine passenger flows and efficiently perform transport services. The main requirements for modeling the city’s passenger traffic are presented. Methods of obtaining data on determining the city’s passenger traffic and public transport are considered. Documentation for the creation of the ISASR (Intelligent System for Automation Search of Routes) project has been developed. The MVC (Model-View-Controller) architectural template was used to develop the project. Class diagrams and precedent diagrams have been developed for the model. Database structures have been created and vehicle types and user types have been defined. Algorithms for finding routes for different situations, with different latitude and longitude coordinates, as well as different number of stops and transfers have been developed.

DETERMINANTS OF CUSTOMER RETENTION IN CELLULAR INDUSTRY OF BANGLADESH: A STUDY ON YOUNG CUSTOMERS

The telecommunication industry in Bangladesh, with the global trends, has been considerably grown up during the last two decades. At the end of December 2020, the number of cellular phone subscribers reached 170.137 million under four operators in the country. The telecommunication industry has been experiencing increased competition because the government has introduced uniform call rates for all operators and launched mobile number portability (MNP) in 2018, resulting in an easy switching for a customer to other operators. This situation has originated a great challenge for the cellular service providers in Bangladesh to retain the existing customers, especially the young subscribers who have more switching trends. The hyper-competition in the industry also induced mobile operators to work hard to attract new customers and retain the existing ones. The paper, therefore, aims to find out the determinants of young customer retention in the cellular industry of Bangladesh. This study is exploratory based on primary data collected from 415 young cellular phone users of the age of below 40 years in the two largest cities (Dhaka and Chittagong) in Bangladesh. The study examined eight determining factors of customer retention using the statistical tools: mean and standard deviation. The study found that price perception, customer satisfaction, switching barriers, and brand image are the strongest customer retention determinants. This study's findings have important implications for cellular phone operators to tailor their services and strategic priorities for maximizing customer satisfaction, loyalty, and retention to gain market growth and higher returns.

Renewable-Energy-Powered Cellular Base-Stations in Kuwait’s Rural Areas

Cellular network operators are actively expanding network coverage and capacity by deploying additional base-stations to provide mobile services to customers in rural areas. The increasing deployment of cellular base-stations has increased the power consumption, energy cost, and associated adverse environmental impact. This paper addresses the feasibility of using renewable energy sources to power off-grid rural 4G/5G cellular base-stations based on Kuwait’s solar irradiance and wind potentials. More importantly, a hybrid renewable energy system will be designed and modeled to meet realistic energy demands of remote base-stations and determine the optimum size of the hybrid system components. Particularly, the hybrid off-grid system may incorporate wind turbines (WTs), photovoltaic (PV) solar panels, converters, a battery bank (BB), and a back-up diesel generator (DG). Two remote cell-sites are considered, namely: (1) Jal-Alayah and (2) Wafra, where the Jal-Alayah cell-site is characterized with higher average wind speed (and wind potential), while the Wafra cell-site has higher average clearness index and solar irradiance. Various hybrid PV/wind electric system (HPWES) configurations are modeled and simulated via HOMER software, with the aim of determining the optimal configuration—in terms of net present cost (NPC)—in each cell-site. Specifically, the simulations have revealed that the WT-BB configuration is the most economical at the Jal-Alayah cell-site while requiring minimal land area and ensuring 100% renewable energy and zero CO2 emissions. This configuration is followed by the PV-DG-BB and PV-WT-DG-BB configurations, where the latter configuration incurs a marginal increase in the NPC than the former but with less land area. On the other hand, the PV-BB configuration is the most cost-effective in the Wafra cell-site; however, in the scenario of limited land area, then the PV-DG-BB configuration can be used but at the expense of slight increase in the NPC and CO2 emissions. This study confirms that utilizing renewable energy sources in two rural areas in Kuwait can be extremely effective in replacing conventional DG-powered base-stations, while minimizing the NPC and CO2 emissions.

LTE-LAA cell selection through operator data learning and numerosity reduction

Long Term Evolution-Licensed Assisted Access (LTE-LAA) architecture is markedly different from traditional LTE HetNets. LTE-LAA deployments also have to contend with interference from coexisting Wi-Fi transmissions in the unlicensed spectrum. Hence, there is a need for innovative cell selection solutions that cater specifically to LTE-LAA. Further, the impact of cell selection on the performance of the existing LTE-LAA deployments should also be investigated through operator data analysis. This work addresses these challenges. We gather a large sample of LTE-LAA deployment data for three cellular operators, i.e., AT&T, T-Mobile, and Verizon, which is analyzed through several supervised machine learning algorithms. We study the effect of cell selection on LTE-LAA capacity and network feature relationships. Insightful inferences are drawn on the contrasting characteristics of the Licensed and Unlicensed components of an LTE-LAA system. Further, a cell-quality metric is derived from operator data and is shown to have a strong correlation with Unlicensed coexistence network performance. To validate the proposed ideas, two state-of-the-art cell association and resource allocation solutions are implemented. Validation results show that data-driven cell-selection can reduce Unlicensed association time by as much as 34.89%, and enhance Licensed network capacity by up to 90.41%. Finally, with the vision to reduce the computational overhead of data-driven cell selection in LAA and 5G New Radio Unlicensed networks, the performance of two popular numerosity reduction techniques is evaluated.

A game theoretic model for cellular network operators’ cooperation under government intervention

In the digital world today, cellular networks and their operators play a competitive and important role in communications. The bases of the competition of operators are the quality of provided services and the coverage level of their antennas, thereby attract customers. This paper studies cellular networks with two old and new operators and under the influence of government intervention in one area. Due to the high cost of building an antenna, the new operator participates in the cost of the infrastructure of the old operator to use the services of these antennas for their customers. On the other hand, the government considers incentive schemes to support mobile operators. The government plans to take part in the infrastructure costs of the old operator, and will receive the income tax from it. Hence, the new operator will go off from paying tax. The government subsidy contract with the old operator is based on the coverage level of the antenna and supports the operator to increase the coverage level. By doing so, the quality level of services and coverage development rate for the old operator increases, leading to increased demand and increased profits for this operator. On the other hand, as government support increases the demand for the old operator, the demand for the new operator decreases and the profit of the new operator decreases. Some numerical examples for Iranian telecommunication companies are applied to examine the applicability of the proposed models. Finally, sensitivity analysis on the main parameters is analyzed in-depth to extract some managerial implications.

The Seismic Early Warning System of Mexico (SASMEX): A Retrospective View and Future Challenges

The earthquake early warning system of Mexico, SASMEX, has 30 years of uninterrupted and successful operation. During this time, the system recorded ∼9,800 earthquakes and broadcast 111 alerts. Alerting was simplified recently, avoiding the emission of two types of alerts. Only earthquakes above a magnitude threshold, dependent on distance to the target city are alerted. SASMEX disseminates early warnings using dedicated receivers, public loudspeakers, multi-hazard radios, and participating TV and radio stations. It is estimated that ∼25 million people receive alert messages from SASMEX. Cell-broadcasting messaging, necessary for the timely delivery of alerts, is not implemented by the local cellular phone operators. The addition of cell phone communication would increase the number of users benefitting from the system. SASMEX does not publish ground motion predictions at the time of issuing the alert.Instead, it distributes a map of peak ground acceleration in Mexico City ∼1 minute after the arrival of strong motion, via electronic messaging. The accepted practice for the population in general is to evacuate at the sound of the alert. This is useful in schools and low-rise buildings, where people are generally drilled to evacuate rapidly. It is not effective in high-rise buildings and where large numbers of people concentrate. Finding protection and not trying to evacuate may be a better option, as it is recommended by other seismic early warning systems. The damaging 19 September 2017 earthquake underlined the difficulties of alerting earthquakes at close distances. Using a different sound of the alert or a countdown may be advisable, so people understand they have less time than normally assumed. There are few social studies on the use of the alert. It is suggested to conduct these studies to explore better ways to use and communicate the seismic alert, including automatic processes to shut down hazardous facilities.

Data Input Quality Metrics on Mobile Positioning Data (MPD)

Statistics Indonesia (BPS) has been using Mobile Positioning Data (MPD) to support official statistics since 2016. As a source of big data, MPD also has veracity characteristics, indicating uncertainty in the data. Therefore, it is necessary to check that the data are good enough to allow further analysis and the quality assurance process. Currently, there is no established international standard for quality assurance of MPD. This paper describes the quality matrix used by BPS in examining data from mobile operators. BPS uses thirteen indicators in conducting quality assurance, where the inspection uses several different methods, such as setting a threshold, checking data completeness, and checking the form of data distribution. Exploratory Data Analysis is carried out to determine whether the data meets the requirements for further analysis. We conducted this research on a mobile network operator data for June - July 2020 as the basis for MPD analysis in 2021. Based on the inspection during this period, BPS can cooperate with this cellular operator to conduct data analysis in 2021. However, the operator must repeat the calculation of the required matrix as quality assurance every month.

Analysis of control models based on dFBA for fed-batch bioreactors solved by interior-point methods

Model-based control of bioprocesses based on dynamic flux balance analysis (dFBA) is an interesting strategy due to the possibility of accounting for wider ranges of cellular behaviour and operation conditions than the one based on unstructured models. This type of control models are bi-level optimization problems, since dFBA comprises of differential equations and a linear programming (LP) model. They can be solved with a nonlinear programming (NLP) solver by replacing the LP model with its first-order optimality conditions (KKT conditions) and discretizing the differential equations. When following this approach, it is important to carefully design the optimization problem considering properties of the selected solver. In this work, we show that we can formulate and solve model-based control models with dFBA for fed-batch bioprocesses by using the KKT conditions of the LP model, relaxing the inequality-multiplier complementarities that arise from these conditions and employing a line search interior point solver. We demonstrate this process on a case study that seeks to the maximize growth of Escherichia coli on glucose and discuss fundamental steps that one should carefully evaluate, such as constraint dependence, handling of complementarities and model initialization.

РАЗВИТИЕ ТУРИНДУСТРИИ: ГЕОАНАЛИТИЧЕСКИЕ ДАННЫЕ КАК ИНСТРУМЕНТ ПРИНЯТИЯ РЕШЕНИЙ

The study is aimed at finding a universal methodology for using geoanalytical data for the purpose of tourism statistics and at analyzing the characteristics of tourism and the touristic trips in Russian territories (Krasnodar region as an example) using data from mobile operators. The authors analyzed aggregated and depersonalized data of mobile operators, generated from cellular networks operation data. Data on cellular subscribers were adjusted for the market shares of mobile operators to obtain indicators that characterize the aggregate number of tourists and excursionists. The analysis allowed, firstly, to offer methodological approaches for working with geoanalytical data and, secondly, to reveal that in 2019-2020 the coronavirus pandemic did not have a significant negative impact on the number of tourists and excursionists which is most likely explained by the switching of citizens from foreign trips to domestic travel. Any impact of the pandemic on the tourists portrait was also not revealed. At the same time, geoanalytical data made it possible to highlight the preferences of Russian tourists in the Krasnodar region to stay in rented housing or live with relatives, friends or in their own housing instead of hotels and inns. During the pandemic these preferences were more explicit, which can be explained by some requirements or recommendations in collective accommodations that may not exist in the private sector. In general, geoanalytical data allow for a more complete and detailed accounting of tourist and excursion flows in a territory for: initiation of support measures for transport infrastructure, tourism development within the framework of state and departmental programs; targeted event planning and creation of related services as part of the tourism strategic development and territory

6G NR-U Based Wireless Infrastructure UAV: Standardization, Opportunities, Challenges and Future Scopes

Providing ubiquitous connectivity for people and equipment with a wide range of service conditions is regarded as the major design goal of 6G networks. Moreover, the future 6G networks need to be extremely flexible to enable reliable and low-latency access for the dynamic number of mobile user equipment. The current base stations (BSs) and remote relay antennas are mostly, on the other hand, installed at fixed geographic locations based on long-term traffic patterns with little re-deployment flexibility. For most 6G applications, where dynamic data traffic occurs in both spatial and temporal domains, such rigid radio access networks (RANs) are unable to maintain ubiquitous connectivity. On the other hand, the rapid advancement of unmanned aerial vehicle (UAV) technology is creating a unique opportunity for the cellular operator to realize flying wireless infrastructure called wireless infrastructure UAV (WI UAV). WI UAV supports wireless connectivity and can stir itself to improve spectral efficiency, coverage, and quality of service of the end-users. This paper introduces 6G new radio in the unlicensed band (NR-U)-based WI UAV that can be used as a base station, relay, or data collector/disseminator. UAV are categorized based on their characteristics, applications, and operations. Further, this paper discusses various regulatory and standardization initiatives for integrating UAVs into the cellular network. Non-standalone NR-U network architecture is designed and explained for WI UAV. Several opportunities and design challenges of NR-U for the WI UAV are discussed and future scopes of WI UAVs are presented.

Methodology for Measuring the Frequency Dependence of Multipath Channels Across the Millimeter-Wave Spectrum

Millimeter-wave (mmWave) communications promise Gigabit/s data rates thanks to the availability of large swaths of bandwidth between 10–100 GHz. Although cellular operators prefer the lower portions of the spectrum due to popular belief that propagation there is more favorable, the measurement campaigns to confirm this – conducted by ten organizations thus far – report conflicting results. Yet it is not clear whether the conflict can be attributed to the channel itself – measured in different environments and at different center frequencies – or to the differences in the organizations’ channel sounders and sounding techniques. In this paper, we propose a methodology to measure mmWave frequency dependence, using the 26.5–40 GHz band as an example. The methodology emphasizes calibration of the equipment so that the measurement results represent the channel alone (and not the channel coupled with the channel sounder). Our results confirm that free-space propagation is indeed frequency invariant – a well understood phenomena but to our knowledge reported nowhere else at mmWave to date. More interestingly, we found that specular paths – the strongest after the line-of-sight path and so pivotal to maintaining connectivity during blockage – are the least invariant compared to weaker diffracted and diffuse paths.

Network Coverage Mapping

Mobile signal strength (coverage) maps are of great importance to cellular operators for network planning and operation are expensive to obtain, imperfectly reflective of call quality outcomes, and potentially constructed from biased samples. Users of smartphones utilize high-speed network services while commuting on public transit and hope to have a consistent, high-quality connection. Measuring and monitoring coverage and quality of service mean being able to assess reality and its representation. Data collection is a vital ingredient in this process. Finding Mobile signal strength and Mapping the Coverage areas using GIS through differentiating the Network. Due to the massive load demand on cellular networks and frequent changes in the underlying radio channel, users often experience sudden unexpected variations in the connection quality. To overcome variations, we create a Map to differentiate the signal strength with a regular update through the Received Signal Strength Indicator (RSSI) in QGIS Software using Python and XML plugins.

Identification and Analysis of a Unique Cell Selection Phenomenon in Public Unlicensed Cellular Networks Through Machine Learning

Cellular operators deploy 4G License Assisted Access (LAA) and 5G NR-U base stations in the unlicensed spectrum, to enhance the overall network capacity. This work highlights a unique Physical Cell Id (PCI) related phenomenon observed in the public LAA operator deployments. Notably, the licensed and unlicensed carriers of a device may have the same PCI or different PCIs. The phenomenon is triggered by the combined effect of unlicensed deployment architectures and cell selection mechanisms. Consequently, the phenomenon will intensify manifold in 5G NR-U whose public deployment will soon commence. Unfortunately, the impact of this phenomenon on coexistence network performance is unexplored. It is also desirable to accurately identify the PCI scenarios at the device for improved cell selection and network performance. However, the data imbalance makes the classification problem challenging. This work addresses these problems through the following approach. Operator data from three LAA cellular providers is gathered and analyzed using machine learning algorithms. The impact of the phenomenon on LTE, LAA, and Wi-Fi components is demonstrated in three steps: First, the variation in network performance prediction accuracy in the PCI scenarios is examined. Second, the efficacy of numerosity reduction techniques used in data-driven cell selection is evaluated in both PCI scenarios. The third step entails a comparison of operator data analysis with network measurements. On-site experiments are conducted at the same PCI and different PCI sites to study the differences in real-time network performance. A controlled LTE-WiFi coexistence environment is created and multiple traffic categories are considered. Finally, a class-weight-based solution is proposed for PCI scenario identification. F-score of 0.75 and AUC-ROC of 0.84 is achieved for LAA, with a minimalist feature-set of SINR and Throughput.

Optimization Analysis of Sustainable Solar Power System for Mobile Communication Systems

Cellular mobile technology has witnessed tremendous growth in recent times. One of the challenges facing the operators to extend the coverage of the networks to meet the rising demand for cellular mobile services is the power sources used to supply cellular towers with energy, especially in remote. Thus, switch from the conventional sources of energy to a greener and sustainable power model became a target of the academic and industrial sectors in many fields; one of these important fields is the telecommunication sector. Accordingly, this study aims to find the optimum sizing and techno-economic investigation of a solar photovoltaic scheme to deploy cellular mobile technology infrastructure cleanly and sustainably. The optimal solar-powered system is designed by employing the energy-balance procedures of the HOMER software tool. The problem objective is considered in terms of cost, but the energy system is constrained to meet the power demand reliably. Process simulations were performed to determine the optimum sizing, performance and monetary cost of the power system, using long-term meteorological datasets for a case study site with defined longitude (31° 25′ E) and latitude (30° 06′ N). From the observed results, the total net present cost (NPC) of the proposed system is $28,187. Indeed, these outcomes can provide profound economic, technical, and ecological benefits to cellular operators. It also ensures a sizeable reduction in greenhouse gas that supports sustainable green wireless network (WN) deployment in remote areas.

Prospects for the Use of Mobile Operator Data in Studies of Natural and Man-Made Risk

The article proposes a conceptual substantiation of the possibility of using the cellular operators data in order to assess the population vulnerability in the natural risk studies. Cellular operators data allow obtaining information on the differentiation of the population vulnerability in a large city (the article provides the case of Moscow) to natural and manmade hazards, taking into account the actual population size and its movement within different time cycles (daily, weekly, seasonal). A reliable information of how many people are at each point of the urban space at a certain point in time will allow not only to timely assess the most likely number of people in the potential danger area, thereby simplifying the work of specialized emergencies services, but also to competently develop a set of preventive measures in accordance with the real need.

The Influence of Product Quality and Price on Customer Loyalty of Telkomsel Users through Customer Satisfaction in Medan City

In this modern era, technology has become something that can be found in almost every part of our daily life. Even in today's era the use of technology is difficult to keep away from human life. With these technological developments and changes, technology is increasingly becoming more effective and efficient. One of them in the development of technology in terms of communication and information. Mobile phones are complementary goods where mobile phones must be equipped with a cellular operator. So the increasing use of mobile phones, the more users of cellular operators will also increase. This increase was also followed by the number of providers in Indonesia, one of which is PT Telkomsel. Today's customers easily switch to competing products if the products and services used by consumers are no longer attractive in the minds of consumers. The factors that influence loyalty are satisfaction, product quality and price. The purpose of this study was to analyze the effect of product quality and price through customer satisfaction on customer loyalty of Telkomsel users. This type of research is associative research and the population in this study is Telkomsel users whose number is unknown. The sampling method used is accidental sampling. Data analysis was carried out through PLS-SEM using the SmartPLS program. Keywords: Product Quality, Price, Satisfaction, Customer Loyalty.