Long Term Evolution Networks Research Articles

Massive Random Access of Machine-to-Machine Communications in LTE Networks: Throughput Optimization With a Finite Data Transmission Rate

This is a sequel of our previous work [20] on access throughput optimization of Machine-to-Machine (M2M) communications in Long Term Evolution (LTE) networks. By incorporating a finite data transmission rate, this paper aims to characterize the effect of data transmission on the optimal access performance of Machine-Type Devices (MTDs). Specifically, both the maximum access throughput and the corresponding optimal Access Class Barring (ACB) factor are obtained as explicit functions of the data transmission rate, which show that even with the ACB factor optimally tuned, the access throughput may deteriorate as the number of MTDs increases, and even drop to zero if the data transmission rate is too small. To boost the data transmission rate, more resources should be allocated to data transmission, which, however, leads to fewer chances for access. In light of the tradeoff between the data transmission rate and the access frequency, the time slot length is further optimized for maximizing the normalized maximum access throughput. Simulation results corroborate that by properly choosing the time slot length, substantial gains can be achieved over the default setting in various scenarios.

Access Delay Optimization of M2M Communications in LTE Networks

This letter focuses on the optimization of access delay performance of Machine-to-Machine (M2M) communications in Long Term Evolution (LTE) networks. Specifically, by deriving the probability generating function of access delay, both the first and second moments of access delay are obtained as functions of system parameters. The mean access delay is further minimized by optimally tuning the Access Class Barring (ACB) factor, and significant gains are demonstrated over the standard setting where the ACB factor is fixed.

Optimal Group Paging Frequency for Machine-to-Machine Communications in LTE Networks With Contention Resolution

Group paging is a baseline solution proposed by the long-term evolution (LTE) standardization body for supporting machine-to-machine (M2M) communications in the current-generation and the next-generation cellular networks. Yet, in conventional group paging scheme, upon the reception of paging message, all machine-type devices (MTDs) in a group will simultaneously access the base station, leading to severe network congestion and intolerably low access efficiency. To handle this issue, in this article, we propose a dynamic group paging mechanism, where only the MTDs with packets to send will join the contention process, and the collisions in the random access channel are addressed by the contention resolution scheme. Explicit expressions of key performance measures including the mean access delay of each MTD are derived as functions of the length of waiting period (interval between two consecutive paging periods) $T_{W}$ , where a smaller $T_{W}$ indicates a higher frequency of group paging. It is shown that $T_{W}$ is a key system parameter that determines the crucial tradeoff between the signaling overheads of the system during the paging period and the access delay performance of each MTD. To study how to properly tune the waiting period length, a utility-based analytical framework is established by taking the aforementioned tradeoff into consideration. The optimal waiting period length for maximizing the network utility is derived and verified by simulation results. The analysis in this article reveals that the network should increase the group paging frequency as the traffic becomes heavier or the number of preambles decreases. Providing more preambles can indeed improve the delay performance, while the gain becomes marginal if the number of preambles is large.

Performance Comparison of ANN Training Algorithms for Hysteresis Determination in LTE networks

Long-Term Evolution (LTE) network is an improved standard for mobile telecommunication system developed by the 3rd Generation Partnership Project (3GPP) requires an efficient handover framework which would reduce hysteresis and improve quality of service (QoS) of subscribers by maximizing scarce radio resources. This paper compares the performance of two ANN prediction algorithms (LevenbergMarquadt and Bayesian regularization) based on received signal strength (RSS) and the hysteresis margin parameters for neuro-adaptive hysteresis margin reduction algorithm. The Bayesian regularization algorithm had a lower mean error when compared with the Levenberg-Marquadt (LM) prediction algorithm and as such a better option for neuro-adaptive hysteresis margin reduction algorithm.

A Comprehensive Distributed Queue-Based Random Access Framework for mMTC in LTE/LTE-A Networks With Mixed-Type Traffic

Thanks to their ubiquitous coverage, Long-Term Evolution (LTE) networks are considered the most potential enabler for massive Machine-Type Communications (mMTC) service in fifth-generation (5G) context. LTE standard, however, was not designed for mMTC and scenarios where the massive Machine-Type Devices (MTDs) population try to access a network over a short period may overload the Random Access CHannel (RACH). Furthermore, there is no mechanism to prioritize urgent MTDs in such overload situation. The baseline Access Class Barring (B-ACB) scheme is thus adopted by the 3GPP to address both issues at a substantial cost of access delay. This paper follows a different approach and proposes a complete solution to the two main issues of cellular mMTC. We promote the use of a mechanism called Distributed Queueing (DQ), aided by a MAC-layer load estimation technique, to effectively resolve contentions between the MTDs to improve delay performance with minimal impacts to LTE access procedure and air interface. Then, by exploiting information related to congestion level from the DQ process, a dynamic access prioritization scheme can be realized without additional signaling overhead. Computer simulation under an mMTC-oriented traffic model shows that our framework outperforms the B-ACB in terms of both access delay and energy consumption when all devices are of equal importance. On the other hand, when devices of different priorities coexist, our framework with proper tuning also offers lower delay for all classes and lower overall energy consumption compared to both the baseline and a dynamic ACB solutions in massive bursty access scenarios.

Performing Analysis of Quality of Service Parameters of 4G Long Term Evolution in Health Care System

Long Term Evolution (LTE) is a leading 4G wireless broadband technology developed by the Third Generation Partnership Project (3GPP) Release 8. LTE is expected to provide very fast, highly responsive and cost effective data services and appears itself to be the right technology at the right time. LTE is becoming an ultimate choice for 4G services around the world due to its higher data rates and lower latency objectives. E-Health services comprise a wide range of healthcare services delivered by utilizing information and communication technology. In order to help existing and emerging e-Health services over converged next generation network (NGN) architectures, there is a need for network QoS control mechanisms that meet the often stringent requirements of such services. The mobile Health (m-health) is currently uniting major academic research worldwide to achieve innovative arrangements in the areas of healthcare. However, there are several challenges and issues that need to be addressed. In this paper we have proposed m-health care system based on 4G LTE network communication instead of existing 3G communication system. We will be monitoring QoS parameters like Delay, Throughput, Jitter, Energy Efficiency and Packet Data Rate for comparing 3G and 4G LTE usage over health care applications. We will further experiment the parameters with graph based analysis.

A review of mobility management entity in LTE networks: Power consumption and signaling overhead

SummaryMobility management (MM) in Long‐Term Evolution (LTE) networks is a vital process to keep an individual User Equipment (UE) connected while moving within the network coverage area. MM Entity (MME) is the LTE component responsible for tracking and paging procedures and controlling the corresponding signaling between the UE and its serving cell, which is necessary for data‐packet exchange. Because of the massive increase in the density of mobile UEs, MME is burdened by the high volume signaling load, especially because most of that load comes from Tracking Area Update (TAU) and Paging messages, which are essential to exchange UE‐specific information with the network. To achieve cost‐efficient resource provisioning, many solutions have been proposed for TAU and Paging management to optimize not only UE experience (ie, battery power consumption) but also network resources (ie, bandwidth). In this paper, we discuss various solution schemes for TAU and Paging in terms of complexity, latency, and computation costs. Also, this review discusses the adverse effects of these solutions on the LTE Key Performance Indicators (KPIs). Furthermore, we present a new trend of MM solutions in LTE networks, called software‐defined network (SDN) and software‐defined virtualization (SDNV). To this end, we examine the existing schemes and challenges in the literature toward next‐generation wireless networks (eg, 5G, Internet‐of‐Things [IoT], and machine to machine [M2M] communications), and we describe user mobility models that are used to analyze the network performance.

QoE-Aware Self-Tuning of Service Priority Factor for Resource Allocation Optimization in LTE Networks

Long Term Evolution (LTE) brings to the theory of advanced leading-edge technologies which guarantees ubiquitous broadband access. As a result, the continuous increment in the number of user terminals (UT) and their expectation leads to the importance of managing and updating the networks as per the expectation of users. In this paper, the concept of self-tuning is used for adjusting the service priority factor in the scheduling algorithms to allocate the resource blocks (RBs) in order to give the appropriate Quality of Service (QoS) based on Quality of Experience (QoE). To access QoE-aware, a QoE model is created by using the Artificial Neural Network (ANN) algorithm to estimate the QoE score by using the QoS parameters. We propose the Particle Genetic Algorithm (PGA) to find the optimal parameter of service priority factors, and the proposed algorithm works efficiently by increasing the average QoE of the network along with maintaining the QoE threshold for each of the multi-service. The detailed comparison is presented between the proposed and reference algorithms to highlight the significance of the proposed algorithm and the simulation and analytical results show that the proposed algorithm outperforms the existing ones in terms of improving the allocation of resources under the environment of limited resources.

A secure cluster-based authentication and key management protocol for machine-type communication in the LTE network

Machine to machine (M2M) communication allows both the wireless and wired systems to communicate with other devices that have the same ability. The security services in mobile networks such as authentication, data confidentiality, and data integrity are mostly performed using cryptographic techniques. For this reason, we propose a secure cluster-based authentication, key management (SCAKM) protocol for machine-type communication in the long-term evolution (LTE) network. The proposed SCAKM protocol consists of the following three phases: clustering, new authentication, and key management. First, efficient clustering is achieved by an optimal spiral dynamic algorithm and CH selection is designed by the different constraints such as communication link quality, communication capability, and energy consumption. Second, authentication is done by the signcryption algorithm that enhances the security between the machine-type communication devices and the core network. Third, key management is done by the updated dynamic group membership management scheme. Moreover, the proposed SCAKM protocol is analyzed and validated by the network simulator (NS2) tool to signify its security against several malicious attacks. Simulation results show that the SCAKM protocol performs better than existing protocols in terms of bandwidth consumption, signaling overhead, transmission cost, and transmission delay.

Online Spectrum Partitioning for LTE-U and WLAN Coexistence in Unlicensed Spectrum

Long-term evolution (LTE) and wireless local area network (WLAN) are often presented as opposing technologies. Hence, efficient partitioning of the spectrum resources carries critical importance for achieving the coexistence of these on the unlicensed spectrum band. In this paper, we firstly develop an online spectrum partitioning algorithm, which needs little signal transmission and exchange between coordination manager and networks. Then, we focus on the convergence analysis of the online spectrum partitioning algorithm, which is difficult due to the time-varying wireless channels. To overcome this challenge, we model the algorithm and network dynamics as the stochastic differential equations (SDE) and show that the algorithm convergence is equivalent to the stochastic stability of a virtual stochastic dynamic system constructed by the SDEs. Then, we give the sufficient condition about the algorithm convergence and the upper bound on the tracking error of the spectrum partitioning algorithm under exogenous variations of time-varying channel state information (CSI). Based on the insights of the impact of time-varying CSI on algorithm convergence, an online compensative spectrum partitioning algorithm is developed to offset the tracking error caused by the disturbance of time-varying CSI. Through performance evaluation, we show that the coexistence performance efficiency will come at low expense of algorithm complexity and signal overhead.

Impact of Scheduling Algorithms for QoS Improvement in LTE Networks

Spectral efficiency and delay are important design metrics that should be taken into consideration while designing a LTE/LTE-A network. Various factors effect these metrics in various ways. In this paper we will be analyzing spectral efficiency vs number of users and delay vs number of users in a 4G LTE network. Additionally, we will be analyzing effect of different scheduling types on delay and throughput of the network. The scheduling types we compare are maximum throughput, proportional fair and round robin. We will conclude on a scheduling type that gives us the least delay and maximum throughput. In view of the Universal Mobile Telecommunications System (UMTS) Long Term Evolution (LTE) technology, this paper considers multiple input multiple output (MIMO) capacity enhancement.

A Data-Driven Traffic Steering Algorithm for Optimizing User Experience in Multi-Tier LTE Networks

Multi-tier cellular networks are a cost-effective solution for capacity enhancement in urban scenarios. In these networks, effective mobility strategies are required to assign users to the most adequate layer. In this paper, a data-driven self-tuning algorithm for traffic steering is proposed to improve the overall Quality of Experience (QoE) in multi-carrier Long Term Evolution (LTE) networks. Traffic steering is achieved by changing Reference Signal Received Quality (RSRQ)-based inter-frequency handover margins. Unlike classical approaches considering cell-aggregated counters to drive the tuning process, the proposed algorithm relies on a novel indicator, derived from connection traces, showing the impact of handovers on user QoE. Method assessment is carried out in a dynamic system-level simulator implementing a real multi-carrier LTE scenario. Results show that the proposed algorithm significantly improves QoE figures obtained with classical load balancing techniques.

Full-Duplex OFDM Radar With LTE and 5G NR Waveforms: Challenges, Solutions, and Measurements

This paper studies the processing principles, implementation challenges, and performance of OFDM-based radars, with particular focus on the fourth-generation Long-Term Evolution (LTE) and fifth-generation (5G) New Radio (NR) mobile networks' base stations and their utilization for radar/sensing purposes. First, we address the problem stemming from the unused subcarriers within the LTE and NR transmit signal passbands, and their impact on frequency-domain radar processing. Particularly, we formulate and adopt a computationally efficient interpolation approach to mitigate the effects of such empty subcarriers in the radar processing. We evaluate the target detection and the corresponding range and velocity estimation performance through computer simulations, and show that high-quality target detection as well as high-precision range and velocity estimation can be achieved. Especially 5G NR waveforms, through their impressive channel bandwidths and configurable subcarrier spacing, are shown to provide very good radar/sensing performance. Then, a fundamental implementation challenge of transmitter-receiver (TX-RX) isolation in OFDM radars is addressed, with specific emphasis on shared-antenna cases, where the TX-RX isolation challenges are the largest. It is confirmed that from the OFDM radar processing perspective, limited TX-RX isolation is primarily a concern in detection of static targets while moving targets are inherently more robust to transmitter self-interference. Properly tailored analog/RF and digital self-interference cancellation solutions for OFDM radars are also described and implemented, and shown through RF measurements to be key technical ingredients for practical deployments, particularly from static and slowly moving targets' point of view.

Cooperative NOMA Broadcasting/Multicasting for Low-Latency and High-Reliability 5G Cellular V2X Communications

To achieve low-latency and high-reliability (LLHR) for the vehicle-to-everything (V2X) services, the Long Term Evolution (LTE)-based solution has been considered as a promising technology. Since the existing LTE networks are based on the orthogonal multiple access (OMA), the limited spectrum resources have not been fully and efficiently utilized, so that severe data congestion and low access efficiency cannot be avoided in dense networks. Non-OMA (NOMA) provides a new solution for 5G V2X services to mitigate traffic congestion and reduce latency. Different from existing works, we propose two relay-assisted NOMA transmission schemes for 5G V2X communications, i.e., half-duplex relay-assisted NOMA (HDR-NOMA) broadcasting/multicasting and full-duplex relay-assisted NOMA (FDR-NOMA) broadcasting/multicasting, and investigate the optimal power allocation problems for them. To improve the quality of service (QoS) for the users with the poor channel conditions and to guarantee the fairness, the power allocation problems are formulated to maximize the minimum achievable rate for all users. Even though both of the formulated problems are neither concave nor convex, it is shown that the considered problems are quasi-concave. Hence, a bisection-based power allocation algorithm is proposed to obtain the optimal solutions to the problems. Numerical results demonstrate that the proposed schemes outperform the scheme with fixed power allocation obviously and achieve a significant performance improvement with respect to a suboptimal fractional transmit power allocation (FTPA) method and the optimized time division multiple access (TDMA) scheme. Besides, compared with HDR-NOMA, better performance can be achieved by FDR-NOMA scheme when the self-interference is sufficiently suppressed.

Feasibility Analysis of LTE-Based UAS Navigation in Deep Urban Areas and DSRC Augmentation.

The current autonomous navigation of unmanned aircraft systems (UAS) heavily depends on Global Navigation Satellite Systems (GNSS). However, in challenging environments, such as deep urban areas, GNSS signals can be easily interrupted, so that UAS may lose navigation capability at any instant. For urban positioning and navigation, Long Term Evolution (LTE) has been considered a promising signal of opportunity due to its dense network in urban areas, and there has recently been great advancement in LTE positioning technology. However, the current LTE positioning accuracy is found to be insufficient for safe UAS navigation in deep urban areas. This paper evaluates the positioning performance of the current network of LTE base stations in a selected deep urban area and investigates the effectiveness of LTE augmentations using dedicated short range communication (DSRC) transceivers through the optimization of the ground LTE/DSRC network and cooperative positioning among UAS. The analysis results based on simulation using an urban canyon model and signal line of sight propagations show that the addition of four or five DSRC transceivers to the existing LTE base station network could provide better than 4–6 m horizontal positioning accuracy (95%) in the selected urban canyon at a position of 150 ft above the ground, while a dense LTE network alone may result in a 15–20 m horizontal positioning error. Additionally, the simulation results of cooperative positioning with inter-UAS ranging measurements in the DSRC augmented LTE network were shown to provide horizontal positioning accuracy better than 1 m in most flight space, assuming negligible time-synchronization errors in inter-UAS ranging measurements.

Analytical Evaluation of Cellular Network Uplink Communications With Higher Order Sectorization Deployments

Higher Order Sectorization (HOS), which splits macro base stations into a larger number of sectors, is widely considered in the cellular community as a cost-effective means of improving network capacity. We develop two general and low-complexity analytical models to characterize and relate the uplink performance indicators with key dynamic functionalities and variables, such as fractional power control (FPC), directional antenna radiation patterns and the multi-cell inter-cell interference (ICI). The adopted methodology approximates the uplink ICIs from individual cell sectors by log-normal random variables, of which the statistical parameters can be estimated using approaches that trade-off complexity and accuracy. Furthermore, the aggregate uplink ICI is approximated with a log-normal random variable, from which network performance metrics are computed. Compared to two existing baseline analytical methods the proposed analytical models have improved accuracy. The analytical models are applied to evaluate HOS deployments with both regular and irregular cell geometries. Results on sectorization scaling show it is an effective method in capacity scaling, but at the cost of increased outage probability. The proposed theoretical models can be used as a fast and effective tool for performance assessment and optimization of Long-Term Evolution (LTE) and 5G networks.

Performance evaluation of video streaming on LTE with coexistence of Wi-Fi signal

The continuous growth in mobile data traffic and limited license wireless spectrum have led to dramatically increase the demand of the radio spectrum. It is widespread the concern about the coexistence of long term evolution (LTE) and Wi-Fi in the unlicensed band. There are several techniques have been proposed to enable the coexistence of LTE and Wi-Fi in the unlicensed band, but these works are targeted on the impact of the LTE to the Wi-Fi network performance. An experiment is carried out in this work to evaluate the impact of Wi-Fi signal on the video streaming in the LTE network. The experimental test comprised of the national instrument (NI) universal software radio peripheral (USRP) 2953R that is controlled by the LabVIEW Communication LTE application framework. Extensiveexperiments are carried out under two scenarios, i.e. (1) Coexistence of LTE and Wi-Fi signal, (2) LTE signal only. Performance evaluations are carried out with different Modulation and coding schemes (MCS) values and different mode of operations, i.e. frequency division duplex (FDD) and time division duplex (TDD) mode. The results illustrated that the interference from Wi-Fi signal caused the performance degradation of the LTE network in throughput and the power received by user equipment (UE).

A Device-to-Device System for Safety and Emergency Services of Mobile Users

The upgraded relay technologies and device-to-device approaches function over the long-term evolution networks and they are being used for location-based systems (LBS). This paper presents a novel LBS for providing the safety support and emergency services for the mobile users such as monitoring, alert, and rescue services are supported especially in the events of natural or man-made disasters. These technologies target the emergency scenarios during and after disasters such as avalanches, and earthquakes.

Mobile Network-Connected Drones: Field Trials, Simulations, and Design Insights

Drones are becoming increasingly used in a wide variety of industries and services and are delivering profound socioeconomic benefits. Technology needs to be in place to ensure safe operation and management of the growing fleet of drones. Mobile networks have connected tens of billions of devices on the ground in the past decades and are now ready to connect the drones flying in the sky. In this article, we share some of our findings in cellular connectivity for low altitude drones. We first present and analyze field measurement data collected during drone flights in a commercial Long-Term Evolution (LTE) network. We then present simulation results to shed light on the performance of a network when it is serving many drones simultaneously over a wide area. The results, analysis, and design insights presented in this article help enhance the understanding of the applicability and performance of providing mobile connectivity to low altitude drones.

Fuzzy-based decisive approach for call admission control in the LTE networks

Long term evolution (LTE) is a user-friendly network in providing a user requested service, and the growth of LTE is reported exponentially due to the attractive applications. The existence of the huge number of users and a massive presence of user demands in the network raise a question on the Quality-of-Service (QoS). In order to assure the required QoS with the available resources in the network, the paper proposes a call admission control scheme using a fuzzy-based decisive approach. The proposed method works based on the available resources in the network and allocates extra resource blocks when there is a lag in the demanded QoS. The flexible and user-friendly service is assured to three groups of users, for which the users are categorized based on the requested service as soon the service is requested. The simulation environment is developed to perform the fuzzy-based call admission control in LTE such that the results prove that the proposed method outperformed the existing methods in terms of delay, throughput, cell power, and call drops. The delay, throughput, cell power, and call drops using the proposed method are 0.1103 s, 1,294,932 bps, 44.2071 dBm, and 328, respectively. Also, the proposed method has the minimum call blocking probability of 0.0527 and 0.2901 for handoff users and new users. It has the call dropping probability of 0.0528 and 0.3357 for handoff users and new users, respectively.