Long Term Evolution Networks Research Articles

Anomalous state detection in radio access networks: A proof-of-concept

Modern radio access networks (RANs) are both highly complex and potentially vulnerable to unauthorized security setting changes. A RAN is studied in a proof-of-concept experiment to demonstrate that an unauthorized network state is detectable at layers in the RAN architecture away from the source of the state setting. Specifically, encryption state is set at the packet data convergence protocol (PDCP) layer in the Long-Term Evolution (LTE) network model and an anomalous cipher-OFF state is shown to be detectable at the physical layer. Three tranches of experimental data totaling 1,987 runs and each involving 285 measurands were collected and used to construct and demonstrate single-feature, multi-feature, and multi-run encryption state detectors. These detectors show a range of performances with the single-feature detector based on reference signal received quality achieving near-0% false alarms and near-100% true detections. Multi-run averaging detectors show similar low-error performance, even just based on marginally effective detector features. The detectors’ performances are studied across the three tranches of experimental data and found by multiple complementary measures to be generalizable provided the testbed protocol is carefully controlled. Essential to these results was an automated, comprehensively instrumented experiment testbed in which measurands were treated as distributions.

A Novel RSS Fingerprint to Locate User Equipment UE in Remote Area

Location determination algorithms are widely used in cellular networks, especially in the long-term evolution (LTE) network, to enable the provision of location-based services (LBS). The increasing global demand for cellular networks has resulted in the creation of new user equipment (UE) positioning systems that align with the network's momentum. Regrettably, all of these technologies are hindered by their incapacity to ascertain the location of the UE in distant regions. This article introduces a novel method utilizing Radio Frequency (RF) fingerprinting to precisely locate UEs in remote areas. The approach entails employing a proposed partitioning model with a high level of precision, incorporating artificial intelligence and machine learning AI/ML in its fundamental state to reduce the search area. Furthermore, two algorithms are suggested: The first aims to enhance the efficiency of the battery with limited capacity by decreasing the frequency of measurements transmission. The second utilizes Jaccard similarity and incorporates the prefix filtering technique to determine matches. The algorithm is used to speed up the process of matching the fingerprint recorded in the fingerprint database with the fingerprint captured in real time. The results shows that it can reduces the transmission rate by 77.08% and achieves the lowest error rate of 35.34 m. Additionally, it exhibits a response time of 8 seconds.

A novel authentication scheme for long-term evolution mobility management: A security investigation

Long-term evolution in wireless broadband communication aims to provide secure communication for users and a high data rate for a fourth-generation network. Even though the fourth-generation network provides security, some loopholes lead to several attacks on the fourth-generation network attacks. The denial-of-service attack occurs when the user communicates with a rogue base station, and the radio base station in fourth-generation long-term evolution networks ensures that the user is attached to the rogue node assigned network. The location leak attack occurs when the packets are sniffed to find any user’s location using its temporary mobile subscriber identity. Prevention of rogue base station and location leak attacks helps the system achieve secure communication between the participating entities. Earlier works in long-term evolution mobility management do not address preventing attacks such as denial-of-service, rogue base stations and location leaks and suffer from computational costs while providing security features. Hence, the present paper addresses the vulnerability of these attacks. It also investigates how these attacks occur and exposes communication in the fourth-generation network. To mitigate these vulnerabilities, the paper proposes a novel authentication scheme. The proposed scheme is simulated using Network Simulator 3, and the security analysis of the proposed scheme is shown using AVISPA –a security tool. Numerical analysis demonstrates that the proposed scheme significantly reduces communication overhead and computational costs associated with the fourth-generation long-term evolution authentication mechanism.

Network Performance and Technological Feasibility of Unmanned Aerial Vehicles for Network Extension

The operational range of conventional and license-free radio-controlled drones is limited due to line-of-sight restrictions (LoS). There exists a definitive method for operating a drone. Consequently, in order to fly the drone beyond the visual line of sight (BVLoS), it is necessary to replace the drone's original wireless communications equipment with a device that requires a licence and is connected to a cellular network. Long-Term Evolution (LTE), a terrestrial communication technique, enables a drone to establish a real-time connection with a ground station. This connection serves the goals of command and control (C&C) as well as payload delivery. Nevertheless, it is important to note that the electromagnetic environment undergoes changes as altitude increases, which can potentially complicate the process of interfacing with drones over terrestrial cellular networks. The objective of this article is to develop a prototype control system for low-altitude microdrones using LTE technology. Additionally, it seeks to assess the feasibility and effectiveness of cellular connectivity for drones operating at various altitudes. This evaluation will be conducted by examining factors like as latency, handover, and signal strength. At a certain altitude, the received signal experiences a decrease in power level by 20 dBm and a degradation in signal quality by 10 dB. The data throughput of the downlink had a fall of 70%, while the latency exhibited an increase of 94 ms. Despite meeting the basic criteria for drone cellular connection, the existing LTE network necessitates enhancements in order to expand aerial coverage, mitigate interference, and minimise network latency.

Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication

In several emergency situations, during natural or human-caused disasters, frontline responders need to be able to communicate and collaborate to properly carry out relief missions. Some countries build their national Public Safety Mobile Broadband based on cellular LTE technology to provide fast, safe, and secure emergency services. However, in several emergency situations, cellular antennas can be overloaded or partially damaged in a manner that affects group communication services. In the last few years, direct device-to-device (D2D) communications have been proposed by the 3GPP as an underlay of long-term evolution (LTE) networks based on proximity, reuse, and hop gains. This paper focuses on a loosely coupled model based on direct D2D communication in a public safety context. Many scenarios related to user membership and network management are detailed. Both the “less cost” and “optimized tree” approaches are proposed and implemented, and their performance is evaluated in terms of the network update number and the resulting average Channel Quality Indicator (CQI). Other optimization approaches, with different CQI thresholds and optimization interval parameters, are simulated to compare their performance with the “optimized tree” approach. By conducting simulations that combine a CQI threshold = 1 and optimization interval = 2 s, it becomes possible to keep an average CQI level close to the “optimized tree” approach, while the costs related to network updates significantly decrease by almost 35%. Other simulations are also carried out to measure the bandwidth required by the control messages between the server and active users. It was found that both inbound and outbound traffic on the server side can be well supported with LTE and 5G networks.

Delay-Aware resource allocation to increase efficiency over LTE network for M2M communications in a noisy channel

Currently, the predominant source of Internet traffic stems from interpersonal user interactions, commonly referred to as user-to-user or H.2.H communications, facilitated through computer systems or mobile devices. The Internet of Things (IoT) is a transformative concept that has gained prominence over the past few years. It represents a paradigm shift from isolated systems to a vast interconnected network of devices that are capable of collecting environmental data, processing it, and making informed decisions based on the processed information. Therefore, it is imperative to have an effective resource scheduler in place for the management of an Long-Term Evolution (L.T.E) network system that encompasses both Machine-to-Machine (M.2.M) devices and Human-to-Human (H.2.H) users. In order to develop analytical formulas for assessing the performance of the suggested programs in respect to average waiting time, average system delay, and average number in the system for M.2.M and H.2.H users, this study uses the M / G / 1 priority queue model. It is possible to provide M.2.M consumers with good Quality of Service (QoS) while maintaining the targeted QoS levels for H.2.H users, according to the analysis of the collected data. The relaxing method is employed to cater to the needs of users in the M.2.M queue. To provide QoS for delay-sensitive traffic generated by H.2.H users, a classification scheme is employed to categorize H.2.H traffic into two primary classes. The findings show that the proposed systems have provided 87.34% improvement in machine-to-machine (M.2.M) performance while managing 92.26% QoS for H.2.H services.

DRX Mechanism for Machine Type Communication (MTC) in Long Term Evolution Network (LTE): Potentials and challenges

Discontinuous Reception (DRX) Mechanism is the power saving technique employed by the Long Time Evolutional (LTE) Network to reduce the power saving. The scheme has seen numerous improvements and tuning to enhance its performance. The Machine Type Communication (MTC) devices have characters that widely differ from the normal devices (Human Type Devices). Therefore, DRX schemes needs to be adjusted to fully accommodate MTC. In this work, a survey of MTC DRX scheme literatures is adapted depicting challenges and potential.

Ascending with ultrasound: telementored eFAST in flight—a feasibility study

PurposeTeleultrasound uses telecommunication technologies to transmit ultrasound images from a remote location to an expert who guides the acquisition of images and interprets them in real time. Multiple studies have demonstrated the feasibility of teleultrasound. However, its application during helicopter flight using long-term evolution (LTE) for streaming has not been studied. Therefore, we conducted a study to examine the feasibility of teleultrasound in an Airbus H145 helicopter.MethodsFour anesthesiologists and one military physician were recruited to perform telementored extended Focused Assessment with Sonography in Trauma (eFAST) during nine helicopter flights, each with a unique healthy volunteer. A radiologist was recruited as a remote expert, guiding the physicians in their examinations. The examining physicians reported the user experience of telementored eFAST on a questionnaire, while the remote expert rated the diagnostic quality of the images on a 1–5 Likert scale. In addition, we measured the duration of the examinations and key LTE network parameters including signal strength, quality, and continuity.ResultsThe images were rated to an average of 4.9 by the remote expert, corresponding to good diagnostic quality. The average duration of telementored eFAST was 05:54 min. LTE coverage was negatively affected by proximity to urban areas and ceased above 2000 ft altitude. Occasional audio problems were addressed by using the Voice over LTE network for communication. The examining physicians unanimously reported on the questionnaire that they would use telementored eFAST on patients.ConclusionTelementored eFAST is feasible in ambulance helicopters and can produce images of good diagnostic quality. However, it relies on stable LTE coverage, which is influenced by many factors, including the helicopter’s altitude and flight path. Furthermore, its benefit on patient outcomes remains to be proven.Graphical abstract

Enhancing LTE Network Performance through Optimized Integrator Handover Algorithm

In the landscape of Long-Term Evolution (LTE) networks, handover algorithms play a pivotal role in maintaining uninterrupted service quality during user mobility. This study revisits the Integrator Handover Algorithm, initially analyzed in previous research, focusing on its optimization in response to the evolving challenges in modern LTE networks. We delve into identifying the inefficiencies of the current algorithm and propose enhancements aimed at improving handover success rates and minimizing latency. Our methodology encompasses a comprehensive literature review, empirical data analysis from contemporary LTE deployments, and a simulation-based approach for algorithm refinement. The outcome is an evolved Integrator Handover Algorithm that is better suited to the high traffic volumes and diverse user behaviors in current LTE network scenarios.

An Overview of Current Challenges and Emerging Technologies to Facilitate Increased Energy Efficiency, Safety, and Sustainability of Railway Transport

This article presents a review of cutting-edge technologies poised to shape the future of railway transportation systems, focusing on enhancing their intelligence, safety, and environmental sustainability. It illustrates key aspects of the energy-transport-information/communication system nexus as a framework for future railway systems development. Initially, we provide a review of the existing challenges within the realm of railway transportation. Subsequently, we delve into the realm of emerging propulsion technologies, which are pivotal for ensuring the sustainability of transportation. These include innovative solutions such as alternative fuel-based systems, hydrogen fuel cells, and energy storage technologies geared towards harnessing kinetic energy and facilitating power transfer. In the following section, we turn our attention to emerging information and telecommunication systems, including Long-Term Evolution (LTE) and fifth generation New Radio (5G NR) networks tailored for railway applications. Additionally, we delve into the integral role played by the Industrial Internet of Things (Industrial IoT) in this evolving landscape. Concluding our analysis, we examine the integration of information and communication technologies and remote sensor networks within the context of Industry 4.0. This leveraging of information pertaining to transportation infrastructure promises to bolster energy efficiency, safety, and resilience in the transportation ecosystem. Furthermore, we examine the significance of the smart grid in the realm of railway transport, along with the indispensable resources required to bring forth the vision of energy-smart railways.

Performability analysis of multi-service call admission control schemes in LTE networks

Two important Quality-of-Service (QoS) measures for cellular networks are the fractions of New Calls (NCs) and Handoff Calls (HCs). These types of calls are usually blocked or dropped for two reasons: radio channels are occupied (performance reason) or failure of radio channels (availability reason). Call Admission Control (CAC) is a technique that accepts or rejects user requests in a network. This paper proposes two new CAC schemes for Long Term Evolution (LTE) networks supporting two classes of services (Real-Time (RT) and Non-Real-Time (NRT)). The proposed CAC schemes are composites, meaning that they consider performance changes associated with the failure and recovery of radio channels. We use the Probabilistic Model Checking (PMC) to perform a comparative analysis between the performability measures (combination of performance and availability measures) of the proposed composite CAC schemes and an existing pure performance model. First, we model the composite CAC schemes with multidimensional Continuous Time Markov Chain (CTMC). Then, we specify QoS requirements through the CTMC using the Continuous-time Stochastic Logic (CSL). Finally, we quantify the performability measures of the composite CAC schemes by checking CSL steady-state, transient, and path formulas using the PRISM model checker.

The Impact of Denial-of-Service Attacks and Queue Management Algorithms on Cellular Networks

In today's digital landscape, Distributed Denial of Service (DDoS) attacks stand out as a formidable threat to organisations all over the world. As known technology gradually advances and the proliferation of mobile devices, cellular network operators face pressure to fortify their infrastructure against these risks. DDoS incursions into Cellular Long-Term Evolution (LTE) networks can wreak havoc, elevate packet loss, and suboptimal network performance. Managing the surges in traffic that afflict LTE networks is of paramount importance. Queue management algorithms emerge as a viable solution to wrest control over congestion at the Radio Link Control (RLC) layer within LTE networks. These algorithms work proactively, anticipating, and mitigating congestion by curtailing data transfer rates and fortifying defences against potential DDoS onslaughts. In the paper, we delve into a range of queue management methods Drop-Tail, Random Early Detection (RED), Controlled Delay (CoDel), Proportional Integral Controller Enhanced (PIE), and Packet Limited First In, First Out queue (pFIFO). Our rigorous evaluation of these queue management algorithms hinges on a multifaceted assessment that encompasses vital performance parameters. We gauge the LTE network's resilience against DDoS incursions, measuring performance based on end-to-end delay, throughput, packet delivery rate (PDF), and fairness index values. The crucible for this evaluation is none other than the NS3 simulator, a trusted platform for testing and analysis. The outcomes of our simulations provide illuminating insights. CoDel, RED, PIE, pFIFO, and Drop-Tail algorithms emerge as top performers in succession. These findings underscore the critical role of advanced queue management algorithms in fortifying LTE networks against DDoS attacks, offering robust defences and resilient network performance.

Towards the use of Unsupervised Causal Learning in Wireless Networks Operation

The current paradigm in Mobile Wireless Networks (MWNs) operation is being defied by the increasing importance of Machine Learning (ML) and Artificial Intelligence (AI). Nevertheless, another paradigm shift is rising with recent developments in causal inference and causal discovery, which, although having the potential to be applied to MWNs, have been relatively unexplored. This paper aims to develop a data-driven methodology using unsupervised ML and Conditional Independence Tests (CITs), typically used in causal discovery tasks, to identify distinct network performance patterns and pinpoint causal factors to explain them. The proposed methodology was first evaluated with crowdsourcing data from User Equipments (UEs). Afterwards, a dataset from a Long-Term Evolution (LTE) network, composed of a set of arbitrary performance indicators and configuration parameters, was considered. The crowdsourcing dataset, containing multiple network speed tests, revealed that the measured uplink throughput contributed the most to the observed performance patterns due to the used Radio Access Technologies (RATs). Furthermore, the LTE dataset revealed a causal relationship between the number of reserved signalling resources in the Physical Uplink Control Channel (PUCCH) and the UE uplink throughput. Notwithstanding, the key contribution of this paper is the consideration of causal-based concepts and methods for network operations enhancement.

Public safety network design for broadband wireless access

Public protection and disaster relief (PPDR) agencies rely on wireless communications to respond in the event of emergencies. Public safety networks (PSNs) provide the wireless network used by emergency services. PSN is used to support push-to-talk services with some data transmission by employing land mobile radios. However, PPDR agencies are increasingly relying on additional information such as videos that require higher bandwidths. Therefore, many countries are transitioning or integrating their public safety networks with advanced broadband wireless communication systems such as fourth-generation (4G) long-term evolution (LTE) and planning to evolve to fifth-generation (5G) new radio (NR) in the future. The paper investigates infrastructure sharing mechanisms and deployment strategies in the transition of PSNs to a 4G LTE network, including a roadmap for cost analysis. Additionally, the paper examines LTE-based PSN deployment scenarios in various countries and engages in a discussion of the advantages and disadvantages of different sharing mechanisms and coexistence schemes. Finally, the challenges within the Public Safety Broadband Network (PSBN) are addressed and potential future research directions in this domain are deliberated.

Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network

Long-Term Evolution (LTE) technology is utilized efficiently for wireless broadband communication for mobile devices. It provides flexible bandwidth and frequency with high speed and peak data rates. Optimizing resource allocation is vital for improving the performance of the Long-Term Evolution (LTE) system and meeting the user’s quality of service (QoS) needs. The resource distribution in video streaming affects the LTE network performance, reducing network fairness and causing increased delay and lower data throughput. This study proposes a novel approach utilizing an artificial neural network (ANN) based on normalized radial basis function NN (RBFNN) and generalized regression NN (GRNN) techniques. The 3rd Generation Partnership Project (3GPP) is proposed to derive accurate and reliable data output using the LTE downlink scheduling algorithms. The performance of the proposed methods is compared based on their packet loss rate, throughput, delay, spectrum efficiency, and fairness factors. The results of the proposed algorithm significantly improve the efficiency of real-time streaming compared to the LTE-DL algorithms. These improvements are also shown in the form of lower computational complexity.

Predictive Modeling of Delay in an LTE Network by Optimizing the Number of Predictors Using Dimensionality Reduction Techniques

Delay in data transmission is one of the key performance indicators (KPIs) of a network. The planning and design value of delay in network management is of crucial importance for the optimal allocation of network resources and their performance focuses. To create optimal solutions, predictive models, which are currently most often based on machine learning (ML), are used. This paper aims to investigate the training, testing and selection of the best predictive delay model for a VoIP service in a Long Term Evolution (LTE) network using three ML techniques: Multilayer Perceptron (MLP), Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). The space of model input variables is optimized by dimensionality reduction techniques: RReliefF algorithm, Backward selection via the recursive feature elimination algorithm and the Pareto 80/20 rule. A three-segment road in the geo-space between the cities of Banja Luka (BL) and Doboj (Db) in the Republic of Srpska (RS), Bosnia and Herzegovina (BiH), covered by the cellular network (LTE) of the M:tel BL operator was chosen for the case study. The results show that the k-NN model has been selected as the best solution in all three optimization approaches. For the RReliefF optimization algorithm, the best model has six inputs and the minimum relative error (RE) RE = 0.109. For the Backward selection via the recursive feature elimination algorithm, the best model has four inputs and RE = 0.041. Finally, for the Pareto 80/20 rule, the best model has 11 inputs and RE = 0.049. The comparative analysis of the results concludes that, according to observed criteria for the selection of the final model, the best solution is an approach to optimizing the number of predictors based on the Backward selection via the recursive feature elimination algorithm.

COST ANALYSIS OF DEPLOYING FEMTOCELLS IN SPEED TRAINS IN NIGERIA TO IMPROVE INDOOR COVERAGE AND CAPACITY OF LTE NETWORK

The cost analysis of deployment of femtocells in speed trains along Abuja-Kaduna standard gauge railway line to improve the coverage and capacity of Long Term Evolution (LTE) network for 5000 commuters using that route daily is presented in this paper. Additional cost will include the cost of femtocells procurement, license, installation, backhaul, operation & maintenance. The cost analysis is carried out using techno-economic model to determine the business viability of embarking on such a project by LTE network providers in Nigeria. The estimated capital expenditure (CAPEX) and operating expenditure (OPEX) as well as the projected revenues accrues from this project form the cash outflows and cash inflows used to simulate the net present value (NPV) and internal rate of return (IRR) in MS Excel environment on a discount rate and tax rate of 22% and 30% respectively to determine the feasibility of the investment. The results of their NPV values which are positive show that the macro-femtocell arrangement in speed train plying AbujaKaduna railway route is cost effective for a life time of 10 years and is feasible up to an IRR 40% which exceeds the estimated discount rate of 22%. The value of the payback period indicates that it will not take long for any LTE network provider in Nigeria to recover their investment. The result of the NPV sensitivity analysis shows how various cost and non-cost parameters affect the costeffectiveness of such investment.

On the Improvement of Cellular Coverage Maps by Filtering MDT Measurements

Cellular networks are constantly evolving, driven by changes in user behavior and device capabilities. To ensure that networks adapt to these changes, it is of vital importance for mobile operators to have a good understanding of how well their network meets subscriber needs. For this purpose, the Minimization of Drive Test (MDT) feature has been standardized, allowing operators the cost-effective provision of geolocated network performance statistics and radio events. However, in practice, positioning errors severely limit the potential of MDT measurements. In this paper, an in-depth analysis of a large MDT dataset taken from a commercial Long-Term Evolution (LTE) network shows for the first time several sources of positioning errors in MDT measurements not previously reported in the literature. To address these, a novel heuristic filtering algorithm is proposed to discard samples with inaccurate location data. Method assessment is done by checking the impact of filtering on the coverage map built with a real MDT dataset. Results show that the proposed filtering method significantly improves the accuracy of coverage maps by eliminating unreliable measurements.

Analysis of LTE Network Quality of Service on Streaming Application

QoS (Quality of Service) measurement of LTE (Long Term Evolution) networks is carried out using the Telkomsel provider. Measurements were made in Perawang, Riau at night. QoS measurements are carried out at Perawang to evaluate and monitor network quality in accessing vidio streaming services provided by the Telkomsel network in the Perawang, Riau. Measurements were aimed at 5 streaming applications, namely YouTube, RCTI+, Online Radio, Twitch, and Vidio. The reason 5 applications are used for QoS measurement is that these applications are popular applications used in the category of Vidio streaming applications. Measurements were carried out for 160 seconds. The measurement results will be analyzed for quality based on the TIPHON standard. The results were obtained by calculating the data that has been obtained by Wireshark. The data will be calculated using the existing equations. Twitch is the only application that has a value of four in the QoS parameter index category and has a QoS value with an excellent index category. 4 other applications have a QoS value with a good index category. This is due to the low value of the throughput index category. Online Radio and Vidio applications have an index category value of 0. YouTube and RCTI+ applications have a throughput index category value of 1 and 2 respectively. For jitter, packet loss, and delay values, 5 streaming applications have an excellent index category with a value of 4. The measurement results that have been obtained would be displayed in the form of tables and graphs.

Predicting the QOE of Video Streaming in Communication Networks

The Video streaming QoE (Quality of Experience) index consists of a series of qualitative factors that are difficult to measure. On the other hand, other Video streaming indices, such as the KPIs (Key Performance Indicators) are easily and physically measurable. 
 
 This paper introduces a method to predict QoE based on KPIs measures in (wired or wireless) communication networks. Besides the possible academic interest, the method may practically be of interest to the network operator. Indeed, to ensure compliance with the SLA (Service Level Agreement) he would like to predict how the QoE can change as a consequence of a new network management alternative. 
 
 To perform prediction, the problem is that the network operator first needs to know how the KPIs would change due to the alternative, and then find a way to derive (say mathematically) the QoE from the new KPIs. 
 
 The contribution of this paper is a simulation/mathematical approach to solving the problem. First,  a simulation method is introduced to know how the KPIs would change as a consequence of the new alternative, and then a valid KPI/QoE mathematical relationship is introduced to derive the new QoE from the new KPIs.
 
 The paper is organized as follows: in Section 1 an introduction is given to the definition of QoE in Video streaming. In Section 2  the status of the art from the literature on the KPI/QoE mathematical models is dealt with, and a valid model is identified that derives the Video streaming QoE from the network KPIs. In Section 3, a simulated network is introduced to know how the KPIs would change as a consequence of a new network management alternative.  Finally, Section 4 uses the identified mathematical relationship to predict the Video streaming QoE from the measured KPIs. 
 
 The considered application is an LTE (Long Term Evolution) network, but the approach can be extended to any communication network, wired or wireless from 3G onwards.