LTE Networks Research Articles

Block-CPS: Blockchain and Non-Cooperative Game-Based Data Pricing Scheme for Car Sharing

This article proposes a blockchain and non-cooperative game theoretic-based secure and optimized data pricing scheme, i.e.,Block-CPS. It aims to secure the data transactions between vehicle owners and customers for rides. It uses the fifth-generation (5G) communication network that offers ultrareliable low-latency communications between vehicle owners and customers. The Interplanetary file system (IPFS) storage protocol used in the proposal reduces the blockchain data storage cost. We then formulated a non-cooperative game-theoretic approach to maximize the profits for vehicle owners and customers. Formulated non-cooperative game is integrated with blockchain to provide security to the Block-CPS. The vulnerability of the developed smart contract is verified and validated using tools like smartcheck and verisol. The performance of Block-CPS is evaluated by comparing it with the traditional approaches using blockchain with 4G and LTE-A networks. The performance evaluation parameters used are system scalability, network latency, data storage cost and its computation, network throughput, profit, communication reliability, and convergence for the optimal payoff between vehicle owners and customers. The performance results shows the Block-CPS outperforms the traditional blockchain-based systems.

Joint Random Forest and Particle Swarm Optimization for Predictive Pathloss Modeling of Wireless Signals from Cellular Networks

The accurate and reliable predictive estimation of signal attenuation loss is of prime importance in radio resource management. During wireless network design and planning, a reliable path loss model is required for optimal predictive estimation of the received signal strength, coverage, quality, and signal interference-to-noise ratio. A set of trees (100) on the target measured data was employed to determine the most informative and important subset of features, which were in turn employed as input data to the Particle Swarm (PS) model for predictive path loss analysis. The proposed Random Forest (RF-PS) based model exhibited optimal precision performance in the real-time prognostic analysis of measured path loss over operational 4G LTE networks in Nigeria. The relative performance of the proposed RF-PS model was compared to the standard PS and hybrid radial basis function-particle swarm optimization (RBF-PS) algorithm for benchmarking. Generally, results indicate that the proposed RF-PS model gave better prediction accuracy than the standard PS and RBF-PS models across the investigated environments. The projected hybrid model would find useful applications in path loss modeling in related wireless propagation environments.

Coalition Game and Blockchain-Based Optimal Data Pricing Scheme for Ride Sharing Beyond 5G

This article proposes a blockchain and coalition game theory-based secure and reliable optimal data pricing scheme for ride sharing. It mainly focuses on securing data sharing between vehicle owners and customers. It employs beyond fifth-generation (5G) as a communication network that facilitates vehicle owners and customers with low latency, high throughput, and high availability for communication. We also formulate a coalition game-theory approach to optimize the payoff of vehicle owners and customers by forming coalitions. The performance of the proposed system beyond 5G is estimated by comparing it with 5G and LTE-A networks. The various performance parameters considered are network latency, throughput, and profit for vehicle owners. The performance results show that the proposed system is efficient and beneficial for both vehicle owners and customers in terms of low latency, high throughput, and profit.

Smoothing-aided long-short term memory neural network-based LTE network traffic forecasting

There is substantial demand for high network traffic due to the emergence of new highly demanding services and applications such as the internet of things (IoT), big data, blockchains, and next-generation networks like 5G and beyond. Therefore, network resource planning and forecasting play a vital role in better resource optimization. Accordingly, forecasting accuracy has become essential for network operation and planning to maintain the minimum quality of service (QoS) for real-time applications. In this paper, a hybrid network- bandwidth slice forecasting model that combines long-short term memory (LSTM) neural network and various local smoothing techniques to enhance the network forecasting model's accuracy was proposed and analyzed. The results show that the proposed hybrid forecasting model can effectively improve the forecasting accuracy with minimal data loss.

Improvements for Uplink Long Term Evolution (UL-LTE) in Heterogeneous Network using Dynamic Spectrum Allocation Technique

Several interference mitigation strategies can be used in LTE networks to meet the consumers' increasing need for faster data speeds. Dynamic Spectrum Allocation (DSA), which maintains spectrum in a converged radio system and distributes it across all participating radio terminals, is one of the most promising solutions. However, the fundamental obstacle to achieving increased network capacities is increased inter-cell interference. The aim of this paper is to analyse the effectiveness of a newly proposed DSA technique by comparing its bit error rate (BER) and throughput (TP) with a typical uplink LTE configuration. To maximise throughput in a heterogeneous network, the suggested DSA technique utilises the Nash Bargaining Solution (NBS) scheme, a cooperative game theory that could correlate with the bit error rate. The software tool GNU Radio, which offers signal-processing blocks to develop softwaredefined radios and signal-processing systems, was used to create the setups. After the simulations were performed, the BER values were recorded and compared, while the TP values were established and computed appropriately. According to the results, the uplink (UL-LTE) architecture with DSA has an improved TP and a lower BER value. As a result, spectral efficiency can also be improved.

Reliability and survivability assessment of LTE-A architecture and networks

Reliability and survivability assessment of LTE-A architecture and networks

The Study of 5G Technology in Metaverse

Abstract: 5G can provide lower latency, greater capacity, and higher speed, than 4G LTE Networks. It is one of the most robust technologies and the fastest the world has ever seen. It means much lower lag, quicker downloads, and a significant effect on how we live, play, and work. The speed of 5G and other connectivity benefits are expected to make businesses more efficient and give end users to access more information faster than ever before. Smart stadiums, Connected cars, and advanced gaming all will depend on 5G networks. In 1992, Neal Stephenson coined metaverse in his novel Snow Crash. It remained buried deep under the snow for several decades, until Facebook announced a virtual reality-powered metaverse to be the next big thing. The connectivity ecosystems and internet devices have been in hysteria ever since the 5g relationship was explained. Nowadays, it is hard to avoid the term. But the truth is, to reach it is full potential the 5G metaverse relationship will be critical. We explore how 5G can enable to be exciting uses like metaverse VR, and metaverse XR and help make Web 3. 0 reality. It is a phenomenon that will give wireless operators the chance to their 5G investments. Facebook is the best example of a metaverse. Before a few months, the Facebook name was changed to Meta. Meta verse means Virtual reality world. In the future Metaverse, the next level of the network going to be used all over the world. It is the 3D version of the virtual world. Nowadays we are using a 4G network for data usage because it is enough for android. In the future people start to use Metaverse for social media usage at that time 4G speed is not enough. It will need a 5G Or 6G data speed connection. So, we have to go to implement that metaverse using in 5G network.

Application of Deep Convolutional Neural Network for Automatic Detection of Digital Optical Fiber Repeater.

The digital optical fiber repeater (DOFR) is an important infrastructure in the LTE networks, which solve the problem of poor regional signal quality. Various types of conventional measurement data from the LTE network cannot indicate whether a working DOFR is present in the cell. Currently, the detection of DOFRs relies solely on maintenance engineers for field detection. Manual detection methods are not timely or efficient, because of the large number and wide geographical distribution of DOFRs. Implementing automatic detection of DOFR can reduce the maintenance cost for mobile network operators. We treat the DOFR detection problem as a classification problem and employ a deep convolutional neural network (DCNN) to tackle it. The measurement report (MR) we used in this paper are tabular data, which is not an ideal input for DCNN. We propose a novel MR representation method that takes the overall MR data of a cell as a sample rather than a single record in the table, and represents the MR data as a pseudo-image matrix (PIM). The PIM will be used as the input for training DCNN, and the trained DCNN will be used to perform DOFR detection tasks. We conducted a series of experiments on real MR data, and the classification accuracy can achieve 93%. The proposed AI-based method can effectively detect the DOFR in a cell.

ANALISIS COVERAGE AREA JARINGAN 4G LTE TELKOMSEL, XL AXIATA DAN INDOSAT OOREDOO DI KECAMATAN BAYUNG LENCIR, KABUPATEN MUSI BANYUASIN, PROVINSI SUMATERA SELATAN.

The research is to analyze the coverage area of ​​the 4G LTE network of telecommunications operators Telkomsel, XL Axiata, and Indosat Ooredo in Bayung Lencir District, Musi Banyuasin Regency, South Sumatra Province, Indonesia. The information obtained by the result of this research is expected to be useful for the public to determine and choose a telecommunications operator that has the widest coverage area and is a catalyst for improvement for telecommunications operators whose coverage area is not yet wide. This study will analyze the parameters of the 4G LTE network coverage area, namely: Reference Signal Received Power (RSRP) and Signal to Noise Ratio (SINR), on the 4G LTE network. The aim of this study to analyze the coverage area of ​​the 4G LTE network of Telkomsel, XL Axiata and Indosat Ooredoo in Bayung Lencir District, Musi Banyuasin Regency, South Sumatra Province using the G-NetTrack Pro application. The study was conducted in 9 villages/kelurahan in Bayung Lencir District, Musi Banyuasin District, South Sumatra Province. The results of the measurement of the coverage area parameters using the G-NetTrack Pro software were then analyzed statistically with the Single Factor Analysis of Variance. The results of the study, that find RSRP and SINR parameters for cellular operators Telkomsel, XL Axiata, and Indosat Ooredoo, it was found that statistically the RSRP value between operators was not significantly different, which means that the RSRP values ​​for Telkomsel, XL Axiata, and Indosat Ooredoo were considered the same and the SINR parameter for operators Telkomsel, XL Axiata, and Indosat Ooredoo are statistically different where the SINR value of Telkomsel is the highest, followed by Indosat Ooredo at position number 2, and XL Axiata at position 3.

Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network

A heterogeneous vehicular network (HetVNET) is a promising network architecture that combines multiple network technologies such as IEEE 802.11p, dedicated short-range communication (DSRC), and third/fourth generation cellular networks (3G/4G). In this network area, vehicle users can use wireless fidelity access points (Wi-Fi APs) to offload 4G long-term evolution (4G-LTE) networks. However, when using Wi-Fi APs, the vehicles must organize themselves and select an appropriate mobile gateway (MGW) to communicate to the cellular infrastructure. Researchers are facing the problem of selecting the best MGW vehicle to aggregate vehicle traffic and reduce LTE load in HetVNETs when the Wi-Fi APs are unavailable for offloading. The selection process utilizes extra network overhead and complexity due to the frequent formation of clusters in this highly dynamic environment. In this study, we proposed a non-cluster adaptive QoS-aware gateway selection (AQAGS) scheme that autonomously picks a limited number of vehicles to act as LTE gateways based on the LTE network’s load status and vehicular ad hoc network (VANET) application’s QoS requirements. The present AQAGS scheme focuses on highway scenarios. The proposed scheme was evaluated using simulation of Urban mobility (SUMO) and network simulator version 2 (NS2) simulators and benchmarked with the clustered and non-clustered schemes. A comparison was made based on the end-to-end delay, throughput, control packet overhead (CPO), and packet delivery ratio (PDR) performance metrics over Voice over Internet Protocol (VoIP) and File Transfer Protocol (FTP) applications. Using VoIP, the AQAGS scheme achieved a 26.7% higher PDR compared with the other schemes.

Modelo de aprendizaje automático para la predicción de calidad en modo estático-inactivo y modo móvil-conectado en redes LTE

In this paper, the main aims are to obtain a quality of signal prediction model in idle-static mode and a quality of connection prediction model in connected-mobile mode, for this, we apply machine learning technique to a real dataset collected from a LTE network deployed at Quito, Ecuador. The proposed models are capable to predict the conditions of low received signal strength and low data rate which is important to select the appropriate method that will most likely offer the highest quality of service. The proposed schemes based on decision tree improves in the idle-static mode and present an accuracy of 67% approximately when compared to the connected-mobile mode, and finally, we propose future works.

Impact of hidden node problem in association and data transmission for LAA Wi-Fi coexistence

Small-cell LTE and Wi-Fi networks has been globally deployed in the unlicensed 5 GHz bands, leading to the need for new coexistence regulations between two very different access technologies. To address coexistence challenges, 3GPP standardized LTE Licensed Assisted Access (LAA) in 5 GHz bands through the incorporation of similar sensing and back-off features. The success of LAA’s fair and efficient coexistence with Wi-Fi can be considered a benchmark for collaborative cellular operation in unlicensed bands. In this paper, we discuss the hidden node scenario when T-Mobile LAA coexists with the Wi-Fi APs that we deployed at the Illinois Institute of Technology (IIT) university campus. In this setup, we observed problems on the Wi-Fi client association and inefficient data transmission when it tries to connect to its corresponding Wi-Fi APs. As LAA is unaware of the Wi-Fi AP’s transmission (low transmission power received at LAA BS), LAA BS continuously transmits with maximum transmission opportunity time, thus highly impacting Wi-Fi performance. Further, the hidden node problem is modeled analytically capturing the root cause of inefficiency.

Evaluation of Electromagnetic Field Generated by LTE and NR Cellular Networks

Evaluation of Electromagnetic Field Generated by LTE and NR Cellular Networks

An intelligent energy efficient handover mechanism with adaptive discontinuous reception in next generation telecommunication networks

Long Term Evolution (LTE) and LTE-Advanced (A) networks have been introduced to accommodate the User Equipment (UEs) that supports high complex technologies such as OFDM, SC-FDMA, MIMO. Recently, 3GPP presented 5G technology to meet the performance requirement of the International Mobile Telecommunication (IMT)-2020. It supports three specific services like Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), massive Machine Type Communication (mMTC). To increase the capacity of the network, 5G will deploy an ultra-dense network in the metro area. It fulfils the spectral efficiency requirement but at the same time increases handover rate. Due to handover, the power consumption of UEs may increase dramatically. To overcome this issue, LTE-A network adopts Discontinuous Reception (DRX) to conserve battery life of UE. 5G networks will also apply evolved variations of this scheme. In general, DRX is composed of some parameters and these need to be optimized during mobility. This paper presents a method for preservation of the battery life of UEs during mobility. The proposed mechanism comprises of three parts. In the first part, Part 1, the operation of normal DRX during mobility is explored. The next parts, Part 2 and Part 3, utilize the machine learning-based methods for the pre-prediction of Target eNB (TeNB) and provide ranks to the UEs during handover. Further, DRX parameters are adjusted according to the dynamic behaviour of the network. Handover latency, end-to-end delay is improved by 20% and 55%, respectively as compared to the conventional scheme. Further, machine learning based (Part 2) and dynamic adjustment of DRX parameters (Part 3) contribute to enhance the power saving factor by maximum up to 85%, and 33% as compared to state of art mechanism.

Reliable Aerial Mobile Communications with RSRP & RSRQ Prediction Models for the Internet of Drones: A Machine Learning Approach.

The unmanned aerial vehicle (UAV) industry is moving toward beyond visual line of sight (BVLOS) operations to unlock future internet of drones applications, including unmanned environmental monitoring and long-range delivery services. A reliable and ubiquitous mobile communication link plays a vital role in ensuring flight safety. Cellular networks are considered one of the main enablers of BVLOS operations. However, the existing cellular networks are designed and optimized for terrestrial use cases. To investigate the reliability of provided aerial coverage by the terrestrial cellular base stations (BSs), this article proposes six machine learning-based models to predict reference signal received power (RSRP) and reference signal received quality (RSRQ) based on the multiple linear regression, polynomial, and logarithmic methods. In this regard, first, a UAV-to-BS measurement campaign was conducted in a 4G LTE network within a suburban environment. Then, the aerial coverage was statistically analyzed and the prediction methods were developed as a function of distance and elevation angle. The results reveal the capability of terrestrial BSs in providing aerial coverage under some circumstances, which mainly depends on the distance between the UAV and BS and flight height. The performance evaluation shows that the proposed RSRP and RSRQ models achieved RMSE of 4.37 dBm and 2.71 dB for testing samples, respectively.

Development of a flexible antenna-wristband for wearable wrist-worn infocommunication devices of the LTE standard

The object of research is the process of radiation of electromagnetic waves from a flexible antenna-wristband. The subject of research is the wave parameters and directional properties of a flexible antenna-wristband. The existing problem is that it is necessary to ensure the electromagnetic compatibility of the radio frequency units of the wrist-worn infocommunication device. This problem is due to the fact that LTE/NB-IoT, Bluetooth/Wi-Fi, and GPS antennas must be placed inside the small-sized case of the infocommunication device. To solve this problem, let’s propose a simple and cheap version of a broadband flexible bracelet antenna for LTE networks, located outside the device case. As a basis for the development of a flexible antenna-wristband, the authors chose a patch antenna, which is the base of the theory of microstrip antennas. This is due to the fact that the theoretical material is well developed for the calculation and study of the patch antenna. Structurally, a patch antenna consists of an upper metal layer that emits electromagnetic waves, a solid dielectric base, and a lower metal layer that acts as a reflector. With the classical approach to constructing a patch antenna, the width and length of its upper layer are commensurate, and its lower metal layer has geometric dimensions much larger than the upper metal layer. In contrast to the classical design, the authors proposed a new shape of the patch antenna, in which the length of the upper layer of the radiation surface is much greater than its width (5–6 times), and the lower metal layer has dimensions slightly larger than the dimensions of the upper layer. The authors have developed a flexible antenna-wristband for the frequency range of 800–1300 MHz with a wave impedance of 50 ohms, 118.7×23 mm of the upper metal layer, and 124.7×25 mm of the lower metal layer. The length of the microstrip feed line of the antenna is 54.6 mm, its width is 2 mm, and the length of the insert is 51.6 mm. The flexible antenna-wristband is connected to the printed circuit board of the infocommunication device by soldering or using a mini-coaxial cable. The authors developed an experimental layout of a flexible antenna-wristband and studied its wave and directional properties. It has been established that in the frequency range 800–1300 MHz the voltage standing wave ratio coefficient of this antenna does not exceed 3.5. The flexible antenna-wristband has directional properties, which allows reducing the level of electromagnetic radiation in the direction of the human body.

A study on propagation models in NS-3 with a case study of LTE network modeling

A study on propagation models in NS-3 with a case study of LTE network modeling

Development of Machine Learning-Based Radio Propagation Models and Benchmarking for Mobile Networks

Path loss prediction plays an important role in assisting network operators to build quality mobile networks by indicating optimal coverage and interference performances. Common empirical models such as the Cost 231-Hata model and Cost 231-Walfisch Ikegami model have been used to predict path loss due to their simplicity and robustness; however, these models do not provide reliable accuracy for small coverage infrastructures, especially in LTE and 5G networks. Therefore, as an alternative method, deterministic models (e.g., ray-tracing models) are widely used for small coverage applications–however, these also require precise 3D digital maps, street structures, and building surface reflection indices, etc. which can be costly and need frequent maintenance. In this paper, an alternative model based on machine learning algorithms is proposed and the algorithms' prediction accuracies are evaluated. The machine learning approach trains models using signal strength data upon which they draw generalizations. The performance of these models is compared to that of the empirical models, and the results reveal that the machine learning models perform significantly better, with Random Forest outperforming other algorithms.

Cascade Forward Neural Networks-based Adaptive Model for Real-time Adaptive Learning of Stochastic Signal Power Datasets

In this work, adaptive learning of a monitored real-time stochastic phenomenon over an operational LTE broadband radio network interface is proposed using cascade forward neural network (CFNN) model. The optimal architecture of the model has been implemented computationally in the input and hidden units by means of incremental search process. Particularly, we have applied the proposed adaptive-based cascaded forward neural network model for realistic learning of practical signal data taken from an operational LTE cellular network. The performance of the adaptive learning model is compared with a benchmark feedforward neural network model (FFNN) using a number of measured stochastic SINR datasets obtained over a period of three months at two indoors and outdoors locations of the LTE network. The results showed that proposed CFNN model provided the best adaptive learning performance (0.9310 RMSE; 0.8669 MSE; 0.5210 MAE; 0.9311 R), compared to the benchmark FFNN model (1.0566 RMSE; 1.1164 MSE; 0.5568 MAE; 0.9131 R) in the first studied outdoor location. Similar robust performances were attained for the proposed CFNN model in other locations, thus indicating that it is superior to FFNN model for adaptive learning of real-time stochastic phenomenon.

Development of a Multilayer Perceptron Neural Network for Optimal Predictive Modeling in Urban Microcellular Radio Environments

Modern cellular communication networks are already being perturbed by large and steadily increasing mobile subscribers in high demand for better service quality. To constantly and reliably deploy and optimally manage such mobile cellular networks, the radio signal attenuation loss between the path lengths of a base transmitter and the mobile station receiver must be appropriately estimated. Although many log-distance-based linear models for path loss prediction in wireless cellular networks exist, radio frequency planning requires advanced non-linear models for more accurate predictive path loss estimation, particularly for complex microcellular environments. The precision of the conventional models on path loss prediction has been reported in several works, generally ranging from 8–12 dB in terms of Root Mean Square Error (RMSE), which is too high compared to the acceptable error limit between 0 and 6 dB. Toward this end, the need for near-precise machine learning-based path loss prediction models becomes imperative. This work develops a distinctive multi-layer perception (MLP) neural network-based path loss model with well-structured implementation network architecture, empowered with the grid search-based hyperparameter tuning method. The proposed model is designed for optimal path loss approximation between mobile station and base station. The hyperparameters examined include the neuron number, learning rate and hidden layers number. In detail, the developed MLP model prediction accuracy level using different learning and training algorithms with the tuned best values of the hyperparameters have been applied for extensive path loss experimental datasets. The experimental path loss data is acquired via a field drive test conducted over an operational 4G LTE network in an urban microcellular environment. The results were assessed using several first-order statistical performance indicators. The results show that prediction errors of the proposed MLP model compared favourably with measured data and were better than those obtained using conventional log-distance-based path loss models.