LTE Networks Research Articles

Usability of Mobile Control Room and CCTV Systems for Crowd Control at Large-Scale Events with the Occurrence of a Large Number of People With Focus on the Czech Republic

This article deals with the issue of the use of modern instruments for crowd control, in particular the use of a mobile closed-circuit television system connected directly to the control room during largescale events. The article has two aims: to check the usability of this type of system especially from the perspective of increasing the safety of event visitors, and to identify the optimal technical solution for crowd monitoring and management and possible obstacles for deployment in the field. The article further focuses on the practical usability of the information obtained through the mobile camera system and the control centre for increasing the safety of event visitors from the perspective of security managers. The conducted investigation identified obstacles to the deployment of a mobile camera system as well as described the optimal parameters for the use of a mobile camera system, and it collected feedback from the security team participating in the security management of the event. The results of the contribution enable easier orientation in the selection of suitable hardware and software for crowd control; Key vulnerabilities in deploying mobile CCTV systems were identified, including issues such as data transmission stability over LTE networks and the financial costs of alternative solutions like satellite connections. Based on field testing and feedback from security teams, recommendations were made to improve the system’s effectiveness and adaptability, leading to more precise decision-making and proactive risk management at large-scale events.

WaveFlex: A Smart Surface for Private 5G CBRS Networks

We present the design and implementation of WaveFlex, the first smart surface that enhances Private 5G networks operating under the shared-license framework in the Citizens Broadband Radio Service frequency band. WaveFlex works in the presence of frequency diversity: multiple nearby base stations operating on different frequencies, as dictated by a Spectrum Access System coordinator. It also handles time dynamism: due to the dynamic sharing rules of the CBRS band, base stations occasionally switch channels, especially when priority users enter the network. Finally, WaveFlex operates independently of the network itself, not requiring access to nor modification of the gNB or UEs, yet it remains compliant with and effective on prevailing cellular protocols. We have designed and fabricated WaveFlex on a custom multi-layer PCB, software defined radio based network monitor, and supporting control software and hardware. Our experimental evaluation benchmarks operational Private 5G and LTE networks running at full line rate. In a realistic indoor office scenario, 5G experimental results demonstrate an 8.58~dB average SNR gain, and an average throughput gain of 10.77 Mbps under a single gNB, and 12.84 Mbps under three gNBs, corresponding to throughput improvements of 18.4% and 19.5%, respectively.

Enhanced RACH optimization in IoT networks: A DQN approach for balancing H2H and M2M communications

A novel adaptive Deep Q-Network (DQN)-based algorithm is designed for the dynamic management of the Random Access Channel (RACH) in LTE networks, facilitating the coexistence of Human-to-Human (H2H) and Machine-to-Machine (M2M) communications. This algorithm employs the integration of user priority and block rate-based dynamic adjustment policies within the DQN framework, significantly enhancing service quality across cellular communications. By categorizing devices into three priority tiers based on their Quality of Service (QoS) requirements, the scheme enables dynamic allocation of RACH resources, thus effectively reducing collisions and enhancing network efficiency. Additionally, the implementation of a dual-criteria convergence check within the model ensures the algorithm’s robustness and reliability, offering a significant advancement in managing the intricate dynamics of M2M and H2H communications. This approach not only exhibits effectiveness in access success rates, reductions in access delay, and increased preamble utilization but also underscores the potential for further refinements in learning efficiency and overall performance through dynamic parameter adjustments. This innovative study offers valuable insights into optimizing RACH resources and sets a solid foundation for advancing intelligent network management in increasingly complex communication landscapes.

Analysis and Prediction of Path Loss for Mobile Communication Lines in Nasarawa State Using Propagation Models

The design of future-generation mobile communication systems depends critically on the path loss prediction methods and their suitability to various signal propagation regions. Even though 5G has been launched in Nigeria, its deployment still faces significant challenges especially in the suburban, and non-urban regions which still depends on 4G. Many operators are still in the process of upgrading their existing 4G networks to support network coverage and higher speed data throughput. Due to the unique mix of Nasarawa State with diverse terrains, in this study, path loss prediction for the key telecommunication networks in Nasarawa State was carried out using Signal Strength Info App. Data was collected for a period of eight weeks (August, 2nd to September, 4th 2023) focusing on 4G LTE networks operating within the 1800 MHz frequency band. Analysis of path loss using empirical models such as Hata, Egli, and COST-231 Hata models were compared with the measured path loss in different terrains of urban, sub-urban, and non-urban environments. The Root Mean Square Error (RMSE) analysis was carried out between empirical and measured path losses. Results shows that, the order of mean predicted path loss at 5km was free space model (BN>CN>DN.AN), Hata model (DN>BN>CN>AN), Cos-231 Hata model (CN>AN>BN>DN), and Egli model (AN>BN>DN>CN). Hate model predicted the highest path loss values while Egli model predicted the lowest values. Overall, Egli model is found to be the most reliable and suitable path loss prediction model for entire Nasarawa State with RMSE value 5.99dBm, 2.90dBm and 3.14dBm for Nasarawa, Keffi and Akwanga LGA respectively. However, Cost-231 Hata model with RMSE value of 5.71dBm is suitable for path loss prediction in Karu due to its irregular terrain. The findings of this study are significant for practical applications in network planning, base station coverage, frequency allocation, and signal strength management, ensuring optimal mobile network performance across various terrains.

Double Deep Q- energy aware Service allocation based on Dynamic fractional frequency reusable technique for lifetime maximization in HetNet-LTE network

The development of mobile communication in heterogeneous networks is incredible in providing various services through wireless cellular communication through advanced long-term evaluation networks. Increasing multi-concern services and frequencies in spectrum channels are highly layered to select the bandwidth to provide the fastest network without interference. Selecting the channel through macro cell selection is essential to improve network communication and provide the quickest service. Most frequency reuse techniques use service optimality and route selection-based protocols to enrich the packet flow. Still, the improper spectrum delights create more delay tolerance due to short-range service optimality due to energy loss by selecting the short spectrum signal to reuse, which doesn't support the lifetime improvement of the LTE network. To resolve these problems, we propose a Double Deep Q- energy-aware Service allocation based on a Dynamic fractional frequency reusable technique for lifetime maximization in the HetNet-LTE network. Initially, the heterogenous communication environment and node deplanement were carried out to construct the LTE network under the WCC. The communication logs are Route Table (RT), and its services are taken by all node LTE Communication Impact Rate (LTE-CIR). Then, the Backhaul Traffic Algorithm (BTA) is applied to predict the interference on traffic rate from the channel frequency margin. Select the balanced node using the Channel Interference Macro Cell Selection (CIMCS) technique. Considering frequency limits with the Double Deep Q- Network (DDQN) approach, energy-aware selects the optimal route to reuse the frequency level using Frequency Domain Packet Scheduling (FDPS) to improve communication. The proposed system improves the overall throughput by up to 97.8 % with adopted channel selection from the macro unit to improve the latency performance. Also, the interference frequency limits are dynamically reused at an energy optimal level with low-level delay tolerance to improve the link stability by up to 98.4 % with higher lifetime maximation in the LTE network.

A set of Models for Device Positioning in Sixth Generation Networks. Part 1. Methods Survey and Problem Statement

Relevance. Today, terahertz radio systems are considered as a technological basis for integrating methods and means of radio communication and radar in promising sixth-generation networks. If in 4G LTE networks the capabilities of positioning user equipment using the infrastructure of base stations were considered as auxiliary options, then in 5G NR networks, location determination technologies (LDTs) have become full-fledged services, the requirements for which are specified along with communication services. A new trend in positioning in 5G NR networks, compared to 4G LTE networks, has become a single-position assessment of the coordinates and orientation of the user equipment based on signals from a single base station with the ability to distinguish between direct and reflected signals. 6G networks are still in their infancy, but it can already be stated that they mark the next stage in the evolution of digital ecosystems, which is characterized by the convergence of communication technologies, localization and sensing of radio air and the surrounding space by radio engineering means.Purpose. This work opens a research cycle devoted to the review of models, methods and algorithms for positioning devices in 6G networks. The goal of the cycle is to find and justify new radio engineering means for achieving decimeter accuracy in 6G device coordinate estimates. The first part of the cycle provides an overview of the methods and formalization of the model for collecting primary measurements.Method is an analytical review of the state of the problem based on current scientific publications, conceptual modeling, categorical approach, expert combination, comparative analysis, formalization, mathematical and simulation modeling.Results. As a result of the review of device positioning methods during the transition to 6G networks, key performance indicators and LDT scenarios are updated. As a result of the comparative analysis of 5G and 6G networks, new factors, advantages and disadvantages of positioning technologies during the transition from millimeter wave networks to terahertz networks are systematized. A formalized mathematical model for collecting primary measurements is used in the simulation model for assessing the accuracy of device positioning in the second part of the cycle.Novelty. This cycle is the first such study in the Russian scientific segment on network positioning of the sixth generation of the terahertz range, in which the author's version provides an overview of methods and a systematization of a set of new factors of the OMP in communication networks.The theoretical significance of the review-analysis lies in the establishment of both technological obstacles and new opportunities for increasing positioning accuracy during the transition to 6G networks.The practical significance of the formalized mathematical model lies in its subsequent software implementation for numerical justification of the limits of positioning accuracy in 6G networks.

Improving 4G LTE network quality using the automatic cell planning

The growing demand for network services leads to an increase in traffic load on eNodeB, resulting in decreased network quality and performance, necessitating optimization. This research analyses the results of optimising 4G reference signal received power (RSRP), signal to interference noise ratio (SINR) and throughput parameters using the automatic cell planning (ACP) method. ACP has been shown to significantly improve the performance and quality of 4G LTE networks compared to traditional cell planning methods. Based on the standard parameter RSRP, increased after ACP optimisation which is dominant in the range ≥ -100 s.d ˃ -85 dBm and obtained an average value of -98.59 dBm with good category. The average SINR has increased by 18.23 dB with a good category. The dominant throughput is in the 14,000 Kbps range with an average value of 50,241.08 Kbps with the excellent category. The ACP method can enhance the performance of 4G LTE networks, potentially addressing operator issues of unstable network quality due to poor coverage. The ACP method significantly enhances 4G LTE network performance, coverage, and user experience, potentially addressing unstable network quality due to poor coverage. This research is crucial for both users and the telecoms industry.

IoT Architecture for Vehicle Pollutant Gas Emission Monitoring and Validation through Machine Learning

This study proposes an IoT architecture for monitoring emissions of polluting gases in vehicles, in response to the growing concern about air pollution and global warming. The architecture is based on a node equipped with DHT22, MQ9, and MQ135 sensors to capture temperature, humidity, and gas emissions, respectively. This node effectively communicates through the LTE network to send the data to the ThingSpeak platform. An analysis of CO2, CO, and CH4 pollution levels is conducted using the collected data. These data are validated through the technical review of a test vehicle. Subsequently, an Artificial Neural Network (ANN) is trained using a specific database of CO2 emissions from vehicles in Canada. As a result, a high R2 of 99.2% is achieved, along with low values of RMSE and MSE, indicating that the model is making accurate predictions and fits well to the training data. The ANN aims to predict CO2 emissions and verify CO2 data from the IoT network. The architecture demonstrates its capability for real-time monitoring and its potential to contribute to pollution reduction.

External Interferences Detection by Examples in LTE Networks for 4G and 5G Networks Efficient Deployments

The relentless growth in wireless traffic forces mobile operators to aggressively increase network capacity to meet subscriber demand. However, spectrum availability is not growing at the same rate as traffic; in fact spectrum resources are in markedly short supply, and, as a result, their cost remain high. The reuse of existing spectrum is a key point for network expansion through capacity densification. But this spectrum reused for network capacity expansion faces the problem of interference in general and external interference in particular which are most often very difficult to detect, identify and eliminate. In this article, we focus on the case of external interferences and on the basis of measurements and experiences obtained in the field, we propose methods and procedures to help identify external interferences and remove them. At the end of the paper a set of recommendations are proposed in other to reduce and cancel external interferences.

Machine learning approach of multi‐RAT selection for travelling users in 5G NSA networks

AbstractThe rapid increment of mobile device usage and the corresponding huge data volume generated afterwards, necessitated the utilisation of the 5G network spectrum. This is deployed today in terrestrial communication in a non‐stand‐alone (NSA) architectural mode; where 5G networks are supported by 4G LTE networks. Hence, the current 5G implementation with the gargantuan number of mobile subscribers, poses challenges to the choice of network Radio Access Technology (RAT) selection between 4G and 5G networks, among available multiple base‐stations to mobile (travelling) users, with respect to their location, bandwidth requirement, and mobility style. Hence, to address the scenario presented above, the authors record live signal measurements of 4G and 5G networks by a travelling user, that transversed multiple 5G NSA base stations. RAT selection implementations were carried out with support vector machine (SVM), deep neural network (DNN), and eXtreme Gradient Boosting (XGBoost) algorithms to select an appropriate RAT between 4G and 5G RATs, for effective resource allocation for travelling users’ requirements. Evaluation of results with standard classification metrics shows XGBoost with overall outstanding accuracy performance at 99.64%.

Support of resourses redistribution in NB-IоT LTE networks

The NB-IoT is expected to be used with the deployed LTE network as well as future 5G networks. The rapid development of the IoT concept has entailed the need to provide wireless communication to a huge number of devices included in the infrastructure. As part of the 5G NR standard for such devices, Massive Machine Communications (m MTC) technology is focused on optimizing the use of network resources to support a large number of stable connections per unit area. The Narrowband Internet of Things (NB-IoT) has been analyzed to enable the connectivity of a wide range of new IoT devices and services to the mobile network. The NB-IoT is also shown to be designed for fixed devices with low data transmission and low consumption, leading to an increase in the number of interconnected devices. In turn, standard NB-IoT modules attempting to simultaneously request radio channel resources for uplink data transmission may suffer from random access preamble collision. The proposed model describes the macro- and microcells of an NB-IoT LTE cluster. An increase in the efficiency of using the bandwidth of networks based on macro- and microcells with a high concentration of users of the NB-IoT LTE networks is shown. Numerical results of an analysis of identifying factors affecting system performance are presented. A linear increase in throughput has been revealed depending on the throughput of the macrocell when using the shared resource of the macrocell for a microcell of equal size without prior repacking of channels when servicing moving subscribers. In the absence of moving subscribers, the additional load is serviced, and the efficiency of using a macro cell increases almost 3 times. In addition, repacking channels significantly increases system throughput.

Analysis and optimization of 4G / LTE network pathloss using Particles Swarm Optimization algorithm

Analysis and optimization of 4G / LTE network pathloss using Particles Swarm Optimization algorithm

Ensemble prediction of RRC session duration in real-world NR/LTE networks

In the rapidly evolving realm of telecommunications, Machine Learning (ML) stands as a key driver for intelligent 6 G networks, leveraging diverse datasets to optimize real-time network parameters. This transition seamlessly extends from 4 G LTE and 5 G NR to 6 G, with ML insights from existing networks, specifically in predicting RRC session durations. This work introduces a novel use of weighted ensemble approach using AutoGluon library, employing multiple base models for accurate prediction of user session durations in real-world LTE and NR networks. Comparative analysis reveals superior accuracy in LTE, with 'Data Volume' as a crucial feature due to its direct impact on network load and user experience. Notably, NR sessions, marked by extended durations, reflect unique patterns attributed to Fixed Wireless Access (FWA) devices. An ablation study underscores the weighted ensemble's superior performance. This study highlights the need for techniques like data categorization to enhance prediction accuracies for evolving technologies, providing insights for enhanced adaptability in ML-based prediction models for the next network generation.

Enabling Uncoordinated Dynamic Spectrum Sharing Between LTE and NR Networks

Enabling Uncoordinated Dynamic Spectrum Sharing Between LTE and NR Networks

The Effect of Overshoot on the Performance of the 4G LTE Network in the East Sawahan Region

The Effect of Overshoot on the Performance of the 4G LTE Network in the East Sawahan Region

Investigation Carrier Aggregation in 4G LTE and 5G NR Networks

Investigation Carrier Aggregation in 4G LTE and 5G NR Networks

Mobile Data Network Performance Review – A Perspective of Zambia

High speed mobile internet results in better user experience and satisfied subscriber. However factors that affect the download speed include transfer technology, number of subscribers sharing the connection, device capability and radio conditions at the subscriber location. This research was conducted to review the mobile internet performance in Zambia and determine its conformity to the regulator, ZICTA, QoS regulation. Two sets of empirical data were collected in the capital city, first in 2021 during the covid pandemic period and second in 2024. The data was compared to check for network operator developments. The results reveal that two MNOs acquired new 2600MHz spectrum, however only one MNO drew maximum benefit resulting in improved 4G download speeds. This improvement was despite an increase in market share over the same period. The second operator that deployed the 2600MHz LTE TDD mode spectrum recorded a degradation in overall downloads speeds when compared to the initial data set in spite of a reduction in market share over the same period. Thus the steps to improve the LTE network are to add more spectrum to at least reach the 3GPP Rel 10 standard of 5 carrier configuration (5CC) to reach a total 100MHz and configure network features like Carrier Aggregation (CA) to provide for higher throughputs for capable devices. Other ways to expand LTE network include add more antennas to allow for higher spectral efficiency using 8 x 8 MIMO or more and thirdly deploy Small cells solution to enhance coverage and capacity in both indoor and outdoor environment.

Optimizing Wireless Networks Performance Through Adaptive Machine Learning Strategies

This research aims to enhance wireless network performance through the development and implementation of a novel machine learning-based algorithm. The methodology involves comprehensive data collection in the Uttarakhand region, specifically in Dehradun, Haridwar, and Rishikesh, through LTE network drive tests. The collected data, over six months, serves as the foundation for the proposed algorithm. The research design focuses on parameterizing essential factors of path loss to optimize coverage. Various drive test tools, including NEMO, Agilent, TEMS, and XCAL-Mobile, are employed to measure and analyze field Received Signal Strength Indicators (RSSI) data. Statistical tools are utilized to collect network parameters, assess coverage, and analyze real-time mobile network data. The research objectives include the development and evaluation of the machine learning algorithm, comparing its performance across diverse network scenarios, and benchmarking against existing state- of-the-art algorithms. The proposed methodology provides a structured and relevant framework to achieve these objectives, fostering advancements in wireless network optimization through innovative machine-learning approaches.

Heartbeating With LTE Networks for Ambient Backscatter

Heartbeating With LTE Networks for Ambient Backscatter

Optimization of 4G LTE Network with Automatic Cell Planning and Carrier Aggregation Methods

The implementation of 4G LTE in Indonesia still faces numerous challenges, particularly in terms of coverage areas that are less than optimal, causing many areas to be classified as bad spots. One of them is Koto Baru Payobasung area, Payakumbuh. Network optimization is one method for overcoming this problem. In this study, we will use optimization techniques such as Automatic Cell Planning (ACP) and Carrier Aggregation (CA) with RSRP, SINR, and Throughput parameters. Through existing simulations, the results of the initial research revealed that two of the three parameters, namely RSRP and Throughput, were classified as very bad conditions. Based on the results of optimization using the ACP technique, it was discovered that there was an increase in the RSRP value of = -103 dBm, SINR of = 2 dB, and Throughput = 15 mbps. The RSRP value for the CA technique is -90 dBm, the SINR is 7 dB, and the throughput is 50 mbps. Because of the value of the optimization parameter results, the CA technique is more effective than the ACP technique in these two optimization techniques