Cellular Data Networks Research Articles

Detection and analysis of cellular network traffic anomalies and SMS spammers

The unfolding usage of mobile users inclusive of both 4G and 5G creates huge accumulation of data in cellular network. The network service providers need to ensure proper management of resource in terms of uninterrupted service with cost-effectiveness. The detection of cellular traffic and Short Message Service (SMS) spammers is very challenging. In this paper a novel method is proposed to analyse and detect the traffic anomalies and SMS spammers. To achieve this, Call Detail Record (CDR) issued by service provider is used. The CDR is pre-processed to convert into machine understandable format using mean-normalization technique. K-means clustering elbow method proves to be the best tool in identifying the traffic clusters in the network that detects both high and low traffic in the network. The novelty of the proposed work is the detection of low traffic clusters which usually is misled as sleeping cell or cell outage. The paper also presents a model designed to predict whether the message is spam SMS or ham SMS. The proposed model is suitable to run different classifiers like Logistic Regression, Multi nominal Naive Bayes, Support Vector Machine (SVM), Random Forest Classifier. The model gives the highest accuracy rate of 98.277% with SVM in detecting SMS spam.

Digital Silk Road; Is it an Anti-Globalization Backlash? Is it Chinese-Style Globalization?

Digital activities of the Belt and Road initiative called the "digital Silk Road" in the post-COVID-19 outbreak era and initially introduced in 2015 as the "information Silk Road," have come under fire. Finding out whether China's technical improvement, which has become evident in many areas thanks to the Digital Belt and Road initiative project, is a backlash against globalization or a new wave of it done in a Chinese form is the main challenge of this study. Data centers—the backbone of the Belt and Road Initiative—undersea fiber optic cables, 5-G cellular data networks, and Turkish investments in these sectors have all been essential in this regard. This method explores China's technological development from the four modernization periods to the present through a chronological descriptive description. This study should be viewed as significantly contributing to the literature because it provides a system-level examination of the digital Belt and Road endeavor.

A Novel Cellular Network Traffic Prediction Algorithm Based on Graph Convolution Neural Networks and Long Short-Term Memory through Extraction of Spatial-Temporal Characteristics

In recent years, cellular communication systems have continued to develop in the direction of intelligence. The demand for cellular networks is increasing as they meet the public’s pursuit of a better life. Accurate prediction of cellular network traffic can help operators avoid wasting resources and improve management efficiency. Traditional prediction methods can no longer perfectly cope with the highly complex spatiotemporal relationships of the current cellular networks, and prediction methods based on deep learning are constantly growing. In this paper, a spatial-temporal parallel prediction model based on graph convolution combined with long and short-term memory networks (STP-GLN) is proposed to effectively capture spatial-temporal characteristics and to obtain accurate prediction results. STP-GLN is mainly composed of a spatial module and temporal module. Among them, the spatial module designs dynamic graph data based on the principle of spatial distance and spatial correlation. It uses a graph convolutional neural network to learn the spatial characteristics of cellular network graph data. The temporal module uses three time series based on the principle of temporal proximity and temporal periodicity. It uses three long and short-term memory networks to learn the temporal characteristics of three time series of cellular network data. Finally, the results learned from the two modules are fused with different weights to obtain the final prediction results. The mean absolute error (MAE), root mean square error (RMSE), and R-squared (R2) are used as the performance evaluation metrics of the model in this paper. The experimental results show that STP-GLN can more effectively capture the spatiotemporal characteristics of cellular network data; compared with the most advanced model in the comparison model on the real cellular traffic dataset in one cell, the RMSE can be improved about 81.7%, the MAE is improved about 82.7%, and the R2 is improved about 2.2%.

Cellular QoE Prediction for Video Service Based on Causal Structure Learning

With the development of telecommunication technology and the popularity of intelligent devices, user experience in cellular network has become the primary factor of concern. At the same time, network operators are plagued by two problems: Prediction of users real-time experience and find network parameters that have a decisive impact on user experience. We proposed a novel scheme for user experience prediction to deal with these two problems. Causal structure learning for cellular networks was used to analyze numerous performance indicators (KPIs) collected from base stations and key quality indicators (KQIs). Through causal structure learning, a directed causal graph based on the association between KPIs and KQI can be obtained. This causal structure can be embedded in graph attention network. Among them, attention mechanism was selected to further strengthen the correlation between parameters. This correlation between each KPI and between KPIs and KQI was used to predict future value of cell level user experience. Results showed that proposed method performance well in cellular network data analysis and user experience prediction.

Artificial Intelligence and Internet of Things-Based Leak Detection Method for the Water Supply Network

The good management and safe operation of the urban water supply network are of great significance to residents’ lives and industrial production. In view of the difficulties in supervision and leakage location of the urban water supply network, based on the technology of Internet of things and artificial intelligence algorithm, a leakage detection method of the urban water supply network is proposed. First of all, low-power, low-cost terminal detection equipment and gateway monitoring equipment are developed for remote data transmission through WiFi or cellular data networks. The data organization, storage, release and control are realized by using the data center software platform. Second, the leakage location model of the water supply network is established by using remote pressure monitoring data, and the accurate location of pipe network leakage is realized. Based on ALO and PSO optimization algorithms, the water supply network in an industrial area of a city in China is solved. Finally, the performance of the two optimization algorithms is compared and analyzed. The results show that the designed intelligent monitoring system of the water supply network can monitor the pipe network well. In addition, on the problem of leakage detection, the ALO algorithm is superior to the PSO algorithm in terms of optimization ability and search efficiency. The leakage monitoring method of water supply networks proposed in this study can provide a reference for the design and management of urban water supply networks.

Toward Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework

The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature to leverage AI for modeling and optimizing network behavior to achieve the zero-touch automation goal. However, to work reliably, AI based automation, requires a deluge of training data. Consequently, the success of the proposed AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of the training data. In this paper, we present an extensive review of classic and emerging techniques to address this challenge. We first identify the common data types in RAN and their known use-cases. We then present a taxonomized survey of techniques used in literature to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The proposed framework builds on available information and combination of techniques including interpolation, domain-knowledge based, generative adversarial neural networks, transfer learning, autoencoders, fewshot learning, simulators and testbeds. Potential new techniques to enrich scarce data in cellular networks are also proposed, such as by matrix completion theory, and domain knowledge-based techniques leveraging different types of network geometries and network parameters. In addition, an overview of state-of-the art simulators and testbeds is also presented to make readers aware of current and emerging platforms to access real data in order to overcome the data scarcity challenge. The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing the appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation.

Off-Policy Learning in Contextual Bandits for Remote Electrical Tilt Optimization

We investigate the problem of Remote Electrical Tilt (RET) optimization using off-policy learning techniques devised for Contextual Bandits (CBs). The goal in RET optimization is to control the vertical tilt angle of antennas at base stations to optimize key performance indicators representing the Quality of Service (QoS) perceived by the users in cellular networks. Learning an improved tilt update policy is hard. On the one hand, coming up with a policy in an online manner in a real network requires exploring tilt updates that have never been used before, and is operationally too risky. On the other hand, devising this policy via simulations suffers from the simulation-to-reality gap. In this paper, we circumvent these issues by learning an improved policy in an offline manner using existing data collected on real networks. We formulate the problem of devising such a policy using the off-policy contextual bandit framework. We propose CB learning algorithms to extract optimal tilt update policies from the data. We train and evaluate these policies on real-world cellular network data. Our policies show consistent improvements over the rule-based logging policy used to collect the data.

Crowdsourced Reconstruction of Cellular Networks to Serve Outdoor Positioning: Modeling, Validation and Analysis

Positioning via outdoor fingerprinting, which exploits the radio signals emitted by cellular towers, is fundamental in many applications. In most cases, the localization performance is affected by the availability of information about the emitters, such as their coverage. While several projects aim at collecting cellular network data via crowdsourcing observations, none focuses on information about the structure of the networks, which is paramount to correctly model their topology. The difficulty of such a modeling is exacerbated by the inherent differences among cellular technologies, the strong spatio-temporal nature of positioning, and the continuously evolving configuration of the networks. In this paper, we first show how to synthesize a detailed conceptual schema of cellular networks on the basis of the signal fingerprints collected by devices. We turned it into a logical one, and we exploited that to build a relational spatio-temporal database capable of supporting a crowdsourced collection of data. Next, we populated the database with heterogeneous cellular observations originating from multiple sources. In addition, we illustrate how the developed system allows us to properly deal with the evolution of the network configuration, e.g., by detecting cell renaming phenomena and by making it possible to correct inconsistent measurements coming from mobile devices, fostering positioning tasks. Finally, we provide a wide range of basic, spatial, and temporal analyses about the arrangement of the cellular network and its evolution over time, demonstrating how the developed system can be used to reconstruct and maintain a deep knowledge of the cellular network, possibly starting from crowdsourced information only.

IRS-Based UAV-Assisted Low-Altitude Passive Relaying: SER Performance Analysis of Optimal Deployment

This paper presents a method for analyzing the symbol error rate (SER) of a UAV cellular data network based on an IRS. This method can change the distances between the BS, IRS, UAV, and the elevation of the UAV to observe their impacts on the SER; then, a two-dimensional curve diagram of the parameters and the SER is drawn to determine the deployment position of the UAV. Finally, by flexibly changing the maximum angle of the UAV’s transmitted signal and the height of the UAV, the coverage area of the UAV’s cellular data network can be obtained through three-dimensional imaging, function fitting, and the extreme value of the condition function. As a relay of a cellular data network, a UAV can be flexibly deployed at any effective position at a low altitude to compensate for the limited coverage of the base station and the poor quality of the user’s received signal. In particular, an IRS deployed on the surface of a building can generate an equivalent line-of-sight channel to reflect the signal from the base station to the UAV. Using an IRS to change the signal phase and amplitude with the flexible deployment of UAVs can give priority to solving the problems of cellular data network coverage and signal quality in urban scenarios.

Popularity based content caching of YouTube data in cellular networks

Popularity based content caching of YouTube data in cellular networks

A novel dual-layer security scheme based on 3-cell cellular neural network encryption and data hiding scheme for color images

A novel dual-layer security scheme based on 3-cell cellular neural network encryption and data hiding scheme for color images

Trip Purposes Mining From Mobile Signaling Data

With the widespread application of mobile phones, it has become possible to study human mobility and travel behaviors based on cellular network data. Contrary to call detail records, the data is triggered by mobile cellular signaling and can provide fine-grained information about users’ daily routines. However, it does not explicitly provide semantic details about traveling traces, e.g., trip purposes. In this paper, we propose a methodological framework to handle large-scale cellular network data and discover the underlying trip purposes in an unsupervised way. We first devise heuristic rules to identify home/work purposes. Then, a flexible latent Dirichlet allocation (LDA) model is presented to discover the activities for remaining trips, in which each trip is depicted by four attributes, i.e. arrival time, age group, stay duration, and the point of interest tag for the destination. Experimental results show that the proposed method can identify diverse trip purposes by explaining their structures over trip attributes and outperform baselines in terms of log-likelihood and perplexity. We also analyze the difference between the automatically discovered trip purposes and those estimated from household census, and the analyzed results demonstrate the feasibility of our proposed method.

A Study of 4G and 5G Networks and its Impact on Mobile Technology

Users may now access the internet while on the move thanks to mobile cellular data networks. One of the hottest areas creating high-speed wireless networks with innovative technology is mobile wireless cellular communication. A radio network technology is a wireless mobile network. In the last 15 years, mobile wireless technology has skyrocketed. Nowadays, the most frequent mode of communication is through a mobile portable device. From the first generation to the fifth generation, wireless technology has been used in mobile communication. The fourth and fifth generations of mobile networks are discussed in this study. We examine the similarities and differences, as well as the advantages and disadvantages of each. We can see how 4G and 5G have influenced us across a variety of industries.

Semi-supervised Mode Classification of Inter-city Trips from Cellular Network Data

Good knowledge of travel patterns is essential in transportation planning. Cellular network data as a large-scale passive data source provides billions of daily location updates allowing us to observe human mobility with all travel modes. However, many transport planning applications require an understanding of travel patterns separated by travel mode, requiring the classification of trips by travel mode. Most previous studies have used rule-based or geometric classification, which often fails when the routes for different modes are similar or supervised classification, requiring labelled training trips. Sufficient amounts of labelled training trips are unfortunately often unavailable in practice. We propose semi-supervised classification as a novel approach of classifying large sets of trips extracted from cellular network data in inter-city origin–destination pairs as either using road or rail. Our methods require no labelled trips which is an important advantage as labeled data is often not available in practice. We propose three methods which first label a small share of trips using geometric classification. We then use structures in a large set of unlabelled trips using a supervised classification method (geometric-labelling), iterative semi-supervised training (self-labelling) and by transferring information between origin–destination pairs (continuity-labelling). We apply the semi-supervised classification methods on a dataset of 9545 unlabelled trips in two inter-city origin–destination pairs. We find that the methods can identify structures in the cells used during trips in the unlabelled data corresponding to the available route alternatives. We validate the classification methods using a dataset of 255 manually labelled trips in the two origin–destination pairs. While geometric classification misclassifies 4.2% and 5.6% of the trips in the two origin–destination pairs, all trips can be classified correctly using semi-supervised classification.

Specific absorption rate reduction for sub-6 frequency range using polarization dependent metamaterial with high effective medium ratio

This research study introduces a multi-layered square-shaped metamaterial (MSM) structure for the electromagnetic (EM) absorption reduction in wireless mobile devices. Usually, wireless devices, for example, a cellular phone emits radiofrequency (RF) energy to the surroundings when used it. Moreover, fast-growing wireless communication technologies that support cellular data networks have also motivated this study. Hence, the focus of the research was to reduce the Specific Absorption Rate (SAR) for the Sub-6 frequency range by designing a multi-layered and compact, 10 × 10mm2 sized metamaterial structure that can be attached inside a mobile phone by avowing any overlapping with existing parts. Overall, six distinct square-shaped metamaterials were constructed on 0.25 mm thick Rogers RO3006 substrate material to reach the target of this investigation. Furthermore, numerical simulations of the proposed metamaterial electromagnetic properties and SAR reduction values were performed by adopting Computer Simulation Technology (CST) Microwave Studio 2019 software. From these simulations, the proposed MSM structure exhibited multi-band resonance frequencies accurately at 1.200, 1.458, 1.560, 1.896 GHz (at L-band), 2.268, 2.683 2.940, 3.580 GHz (at S-band) and 5.872 GHz (at C-band). Simultaneously, the proposed MSM structure was simulated in High-Frequency Structure Simulator (HFSS) to authenticate the numerical simulation data. The comparison of simulation data shows that only the primary and last resonance frequencies were reduced by 0.02 and 0.012 GHz, whereas the rest of the frequencies were increased by 0.042, 0.030, 0.040, 0.032, 0.107, 0.080, and 0.020 GHz in sequential order. In addition, the introduced MSM structure manifests left-handed behaviour at all the resonance frequencies. Nevertheless, the highest recorded SAR values were 98.136% and 98.283% at 1.560 GHz for 1 g and 10 g of tissue volumes. In conclusion, the proposed MSM met the objectives of this research study and can be employed in EM absorption reduction applications.

Delay Factors Modelling for Real-Time Traffic Information Systems

The paper is presenting an overview of the factors that lead to critical delays which may occur in multi-modal realtime traffic and travel information systems (MRTTI) employing cellular data network communications. The analysis is taking into account MRTTI systems that use mobile devices to detect position and speed of a vehicle and receive traffic information furthermore used to adjust a previously defined route from origin to destination points. Information transmission delays are also investigated for route guidance systems used in critical applications, such as emergency vehicles management. The identification of the delay factors and some models regarding the delays are presented along with measurements on a real system.

Improving performance and data transmission security in VANETs

This article proposes a new approach to achieve fast and reliable transfer of data and uses machine learning techniques for data processing to improve the performance and data transmission security of the vehicular network. The proposed approach is the combination of 5G cellular network and alternative data transmission channels. The data collection experiment took place within different areas of the city of Berlin over a 3-month time period and involved the use of 5G technologies. The study carried out the analysis and classification of big data with the help of position-based routing protocols and the Support Vector Machine algorithms. The said techniques were employed to detect non-line-of-sight (NLoS) conditions in real time, which ensure the secure transmission of data without the loss or degradation of network performance. The novelty of the work is that it tackles various traffic scenarios (the extent of road congestion can affect the quality of big data transmission) and offers a way to improve big data transfer using the Support Vector Machine technology. The study results show that the proposed approach is effective enough with big data and can be employed to improve the performance of urban VANET networks and data transmission security. The study results can be useful in developing high-performance 5G-VANET applications to improve traffic safety in urban vehicular environments.

CellSense

Data from the cellular network have been proved as one of the most promising way to understand large-scale human mobility for various ubiquitous computing applications due to the high penetration of cellphones and low collection cost. Existing mobility models driven by cellular network data suffer from sparse spatial-temporal observations because user locations are recorded with cellphone activities, e.g., calls, text, or internet access. In this paper, we design a human mobility recovery system called CellSense to take the sparse cellular billing data (CBR) as input and outputs dense continuous records to recover the sensing gap when using cellular networks as sensing systems to sense the human mobility. There is limited work on this kind of recovery systems at large scale because even though it is straightforward to design a recovery system based on regression models, it is very challenging to evaluate these models at large scale due to the lack of the ground truth data. In this paper, we explore a new opportunity based on the upgrade of cellular infrastructures to obtain cellular network signaling data as the ground truth data, which log the interaction between cellphones and cellular towers at signal levels (e.g., attaching, detaching, paging) even without billable activities. Based on the signaling data, we design a system CellSense for human mobility recovery by integrating collective mobility patterns with individual mobility modeling, which achieves the 35.3% improvement over the state-of-the-art models. The key application of our recovery model is to take regular sparse CBR data that a researcher already has, and to recover the missing data due to sensing gaps of CBR data to produce a dense cellular data for them to train a machine learning model for their use cases, e.g., next location prediction.

A hybrid of neuro-fuzzy inference system and hidden Markov Model for activity-based mobility modeling of cellphone users

The aim of this paper is to develop an activity-based travel demand model by receiving cellular network data. Our contribution is to model the uncertainty of human behaviors and also the ambiguity in features affecting users’ activities. We used probabilities to model the first aspect and fuzzy theory to treat with the second; therefore, a hybrid model is proposed based on the Hidden Markov Model (HMM) and Fuzzy Inference System (FIS) such that FIS is used in the emission model of HMM. To show the efficiency of this model, we applied the model to the data collected by Irancell operator and validated the results with four different data sources; labeled data collected from volunteers, ground truth data labeled by an expert, activity-based number of trips generated from/attracted to different regions and reported traffic volume of highways. We have shown that the activity recognition accuracy of the model is 83% and an average error of 5% is obtained when comparing the statistics of the model generated activity plans and the corresponding statistics provided in reports. Generated activity plans are also converted to traffic volumes on transportation network links through MATSIM simulation software and the promising R2 value of 0.83 is observed.

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

With the advent of the pandemic (e.g., novel corona virus disease 2019 (COVID-19)), a tremendous amount of data about individuals are collected by the health authorities on daily basis for curbing the disease’s spread. The individuals’ data collection/processing at a massive scale for community well-being with the help of digital solutions (e.g., mobile apps for mobility and proximity analysis, contact tracing through credit card usage history, facial recognition through cameras, and crowd analysis using cellular networks data etc.) raise several privacy concerns. Furthermore, the privacy concerns that are arising mainly due to the fine-grained data collection has hindered the response to tackle this pandemic in many countries. Hence, acquiring/handling individuals data with privacy protection has become a vibrant area of research in these pandemic times. This paper explains the shift in privacy paradigm due to the pandemic (e.g., COVID-19) which involves more and detailed data collection about individuals including locations and demographics. We explain technical factors due to which the people’s privacy is at higher risk in the COVID-19 time. In addition, we discuss privacy concerns in different epidemic control measures (ECMs) (e.g., contact tracing, quarantine monitoring, and symptoms reporting etc.) employed by the health authorities to tackle this disease. Further, we provide an insight on the data management in the ECMs with privacy protection. Finally, the future prospects of the research in this area tacking into account the emerging technologies are discussed. Through this brief article, we aim to provide insights about the vulnerability to user’s privacy in pandemic times, likely privacy issues in different ECMs adopted by most countries around the world, how to preserve user’s privacy effectively in all phases of the ECMs considering relevant data in loop, and conceptual foundations of ECMs to fight with future pandemics in a privacy preserving manner.