Long Term Evolution System Research Articles

A Sparsity Adaptive Algorithm to Recover NB-IoT Signal From Legacy LTE Interference

As a forerunner in 5G technologies, Narrowband Internet of Things (NB-IoT) will be inevitably coexisting with the legacy Long-Term Evolution (LTE) system. Thus, it is imperative for NB-IoT to mitigate LTE interference. By virtue of the strong temporal correlation of the NB-IoT signal, this letter develops a sparsity adaptive algorithm to recover the NB-IoT signal from legacy LTE interference, by combining K-means clustering and sparsity adaptive matching pursuit (SAMP). In particular, the support of the NB-IoT signal is first estimated coarsely by K-means clustering and SAMP algorithm without sparsity limitation. Then, the estimated support is refined by a repeat mechanism. Simulation results demonstrate the effectiveness of the developed algorithm in terms of recovery probability and bit error rate, compared with competing algorithms.

A Multiwideband Compact Antenna Design for Vehicular Sub-6 GHz 5G Wireless Systems

This article presents a multiwideband monopole antenna for automotive application in the long-term evolution (LTE) and 5G systems that covers the frequency range from 617 MHz to 5 GHz with reasonable rejection on the L1/L2/L5 GNSS bands. The antenna is suitable for placement on a car roof within a shark-fin radome due to its physical dimensions and performance. The antenna is simulated using software and then fabricated and measured on a 1 m ground plane and a vehicle roof. The measurement results of the fabricated prototype are presented and plotted in terms of reflection coefficient, radiation patterns, antenna efficiency, and linear average gain (LAG). The proposed antenna shows very good properties that make it suitable for the automotive industry.

PREDICTABLE SECURE CHARACTERISTICS AND STANDARDS, FUTURE PROOF FIFTH GENERATION (5G) ENABLEMENT - WHY PRIVATE LONG TERM EVOLUTION (LTE) IS THE CHOICE FOR DISTRIBUTION AUTOMATION

This paper presents a Western Power Distribution network innovation allowance (NIA) project that utilises Long Term Evolution (LTE) technology for multi service and multi-vendor connectivity to a range of distributed energy network assets. The trial LTE system covers a 25km radius from the central base station located in Taunton, South West England on the Western Power Distribution (WPD) network. Greater awareness of distributed energy network flows, more active management and interconnectivity control are creating more challenges as the power network facilitates flexibility in services by dynamically adding, removing power or time shifting energy demand customers. In order to manage this new energy landscape and support legacy assets, new communication technologies must be investigated. As the electricity distribution system evolves, there is a need for enhanced real time monitoring and control to facilitate real time asset monitoring. This communication must support many application characteristics: throughput, quality of service, latency and redundancy while balancing economic and investment dimensions in order to deliver maximum value for the end service user.

Singularity analysis of igneous zircon U-Pb age and Hf isotopic record in the Zhongdian arc, northwest Yunnan, China: Implications for Indosinian magmatic flare-up and the formation of porphyry copper deposits

Situated in the southern part of Yidun arc in the southeastern Tibetan Plateau, the Zhongdian arc records magmatic activity that ranges from approximately 270 to 190 Ma, which is likely attributable to subduction of the Garze-Litang slab and involved steady low-volume magmatic lulls punctuated by short-lived high-volume flare-ups. Many porphyry copper deposits have been found in these magmatic rocks, but the temporal-spatial relationships among magmatism, porphyry copper deposits, and subduction processes around the Zhongdian arc have long been debated. Frequency distributions calculated from igneous zircon U-Pb ages are commonly utilized to interpret the duration of magmatic events. Recently, a new concept of fractal density and a local singularity analysis (LSA) method have been applied to analyze the geometric properties of zircon U-Pb age peak anomalies, which are linked to deeply rooted avalanches associated with short spurts of convection during the formation of supercontinents and continental crust growth. In this paper, the igneous zircon U-Pb dating data collected from the Zhongdian arc are analyzed by the LSA method from the perspective of fractal density to interpret the causational relationship between age peak and subduction process of the Garze-Litang oceanic crust. The results show that the age density around the age peak of 215 Ma can be well fitted and described by power law functions, and the strong singularity (k > 0.8) indicates the occurrence of extreme geological events. Meanwhile, systematic changes in the hafnium isotopic composition of igneous zircons with time reflect the upwelling of asthenospheric mantle materials, characterized by a rapid increase in εHf(t) values that reach the highest around the 215 Ma peak. Moreover, the log–log plot that shows the cumulative number-age distribution reveals another transitional age of 235 Ma. By comparison of the magmatic intensity between the western and eastern belts of the Zhongdian arc, it is indicated that the eastward magmatic migration might have begun at ca. 235 Ma. Combined with the previous work, we propose that the Indosinian magmatic flare-up in the Zhongdian arc might have been caused by superimposition of slab subduction, steepening of slab dip, and slab breakoff. This long-term magmatic hydrothermal evolution system and extreme singularity events also provide indispensable conditions for the formation of large-scale and high-grade porphyry copper deposits in the region. Our findings provide new insights into the subduction process of the Garze-Litang oceanic crust and Indosinian magmatic framework around the Zhongdian arc. It is believed that the LSA method has potential applications in depicting the variation of magmatic activity and predicting mineral resources.

Adjusting Large-Scale Propagation Models for the Amazon Region Using Bioinspired Algorithms at 1.8 and 2.6 GHz Frequencies

In this work, a new method employs Bioinspired Computational (BIC) optimization from the genetic algorithm, bat algorithm, and flower pollination algorithm. Robust and accurate modeling of the input parameters adjusts the propagation models Stanford University Interim, Electronic Communication Committee, and Floating Interception that consider environments with characteristics specifically of urban regions in the Amazon. The lack of research related to the development of propagation models for Amazonian environments motivated this work. Thus, this application proves the effectiveness of using BIC techniques for modeling the communication channel. Measurement campaigns were carried out in the city of Belem, Brazil, for large-scale channel modeling on the frequencies of 1.8 and 2.6 GHz, belonging to the long-term evolution or fourth-generation mobile communications system (4G). After being adjusted by the optimum values calculated by the BIC techniques used, the models showed better results compared to modeling without optimization. Additionally, it was verified an error reduction of about 80% concerning the metrics root-mean-square error and standard deviation.

Lightweight Physical Layer Aided Key Agreement and Authentication for the Internet of Things

In this paper, we propose a lightweight physical layer aided authentication and key agreement (PL-AKA) protocol in the Internet of Things (IoT). The conventional evolved packet system AKA (EPS-AKA) used in long-term evolution (LTE) systems may suffer from congestion in core networks by the large signaling overhead as the number of IoT devices increases. Thus, in order to alleviate the overhead, we consider cross-layer authentication by integrating physical layer approaches to cryptography-based schemes. To demonstrate the feasibility of the PL-AKA, universal software radio peripheral (USRP) based tests are conducted as well as numerical simulations. The proposed scheme shows a significant reduction in the signaling overhead, compared to the conventional EPS-AKA in both the simulation and experiment. Therefore, the proposed lightweight PL-AKA has the potential for practical and efficient implementation of large-scale IoT networks.

Cell Selection Mechanism Based on Q-learning Environment in Femtocell LTE-A Networks

Universal mobile networks require enhanced capability and appropriate quality of service (QoS) and experience (QoE). To achieve this, Long Term Evolution (LTE) system operators have intensively deployed femtocells (HeNBs) along with macrocells (eNBs) to offer user equipment (UE) with optimal capacity coverage and best quality of service. To achieve the requirement of QoS in the handover stage among macrocells and femtocells we need a seamless cell selection mechanism. Cell selection requirements are considered a difficult task in femtocell-based networks and effective cell selection procedures are essential to reduce the ping-pong phenomenon and to minimize needless handovers. In this study, we propose a seamless cell selection scheme for macrocell-femtocell LTE systems, based on the Q-learning environment. A novel cell selection mechanism is proposed for high-density femtocell network topologies to evaluate the target base station in the handover stage. We used the LTE-Sim simulator to implement and evaluate the cell selection procedures. The simulation results were encouraging: a decrease in the control signaling rate and packet loss ratio were observed and at the same time the system throughput was increased.

Doppler-tolerant sequence design for positioning high-speed vehicles in millimeter-wave cellular systems

Future fifth-generation (5G) New Radio (NR) services consider positioning an integral part of a system that provides high precision using millimeter-wave (mmWave) cellular networks. While the 5G standard for positioning is not fully defined yet, the positioning reference signal (PRS) used in long-term evolution (LTE) system is being considered. Since the conventional LTE PRS is designed for LTE systems below 6 GHz, it is more sensitive and inefficient for high-speed vehicle (HSV) experiencing a high Doppler effect in mmWave cellular systems. In this paper, a new sequence, called an oscillatory pulse sequence (OPS), is proposed for the positioning of HSVs in mmWave cellular systems. The ambiguity function (AF) and cross-ambiguity function of OPS are analyzed to examine its autocorrelation and cross-correlation properties in the existence of Doppler shift. The performance of the proposed OPS is compared with the performance of LTE PRS, Zadoff-Chu, and linear-frequency modulated waveforms with respect to correlation, time ambiguity, and Doppler tolerance. The analytic and simulation results show that the OPS provides an accurate timing (distance) estimation in the existence of a high Doppler shift, which is the ideal characteristic of the AF for positioning. The OPS is especially suitable for orthogonal frequency division multiplexing (OFDM) systems because it can be easily multiplexed with other signals in the frequency domain.

Joint Statistical and Machine Learning Approach for Practical Data-Driven Assessment of User Throughput Quality in Microcellular Radio Networks

The provision of enhanced user experience with higher-data transmission speed over the radio frequency channels was one of the core objectives of evolving Long Term Evolution (LTE), which is a 4th generation of cellular broadband communication standard. LTE was developed to supports data transmission speed up to 300 Mbps and 75 Mbps in downlink and uplink, respectively, utilizing a robust multiplexing technique. For mobile radio networks operators to maintain high operability around the ever increasing and demanding subscribers, periodic evaluation, quantitative estimation and analysis of network performance is pivotal. There exist a quantum of previous research works on LTE system network performance that have been conducted both at spatial domain and temporal domain, but the authors concentrated their studies either on practical LTE radio coverage issues only or on general LTE network performance issues using analytical/simulation techniques. In this contribution, a combined statistical and machine learning approach is proposed and applied to provide an in-depth user data throughput performance of operational LTE networks in relation to signal coverage and signal quality parameters, using in typical microcellular built-up terrains as case study. Specifically, the impact of transmitter- receiver communication distance, received signal reference power, received Signal reference quality, Signal to Noise Ratio, Received Signal Strength and Channel Quality Indicator on user throughput over Radio Link Control, Physical Downlink Shared Channel and Packet Data Convergence Protocol layers have been shown using professional TEMS investigation tools. Particularly, for the purpose of effective LTE system network planning and management, the most influencing signal coverage and signal quality parameters on user throughput performance are also examined and shown in the framework of Self-organsing map (SOM) and statistical correlation stratagems.

Precoding Matrix Selection for LTE System

In the current 4G Long Term Evolution (LTE) system, it adopts the orthogonal frequency division multiplexing (OFDM) and multiple input multiple output (MIMO) schemes to improve the spectral efficiency and increase the data rate for the system. Besides, precoding is an effective technique in MIMO transmission for the transmitter (Tx) to use a suitable precoding matrix in accordance with the change of the channel. With the proper selected precoding matrix, MIMO system can reduce the multiple access interference and increase the received signal to noise ratio (SNR) at the receiver (Rx). In this paper, two precoding matrix selection methods are proposed via using the features of the beam patterns of the adopted precoding matrices for 3GPP LTE system. With a little performance degradation, the first proposed method has the advantage of low computational complexity and the second method can have the zero latency property compared with the conventional full search methods.

Implementation of Efficient Stopping Criteria for Turbo Decoding

The cellular systems with 4th generation, Long Term Evolution (LTE) standard have been transmitted the data at higher rates than the 3G, and 2G systems, in an ever-crowded frequency spectrum. This transmission needs more accuracy, high reliability, and throughput with a low area and power consumption. Therefore, it requires an efficient error control coding. Turbo code is used for LTE system for its good error correction ability at low signal to noise power ratio which can approach Shannon limit performance for large frame lengths. This paper presents a simple and efficient modification that can be applied to all present stopping criterion (SC) to achieve the quality of service mentioned previously. It’s proved that using a trimmed sequence of log-likelihood values (LLR) instead of the full length in the algorithms of stopping the iterative decoding has a significant impact on the utilized silicon area and throughput without a significant sacrifice in performance. It also presents a comparison of designing the Soft-Output Viterbi (SOVA) decoder using different arbitrary-precision fixed data types that offers by Vivado high-level synthesis (HLS) instead of the costlier float representation to reduce processing time and consumed area. Due to its high flexibility in designing and implementing prototype systems, the FPGA device (Kintex-7, Xilinx part number XC7K325T-2FFG900C) was utilized with different parallelism and loop pipelining directives to ensure acquiring the targeted initiation interval and silicon area.

Seven Challenges for Communication in Modern Railway Systems

To meet the increasing demands for passenger data rates, modern railway communication networks face significant challenges. The advent of 5G communications after the long-term evolution (LTE) and LTE-Advanced (LTE-A) systems provides several technological advances to address these challenges. In this paper, after reviewing the main 5G communication aspects for modern railways, we describe seven main challenges faced by train connectivity, and discuss appropriate solutions. Specifically, we elaborate on techniques for ensuring connectivity and energy efficiency for the passengers' user equipment (UE) through the use of mobile relays (MRs) on top of the train wagons in conjunction with intelligent resource allocation.

On Efficient DCT Type-I Based Low Complexity Channel Estimation for Uplink NB-IoT Systems

Channel estimation is a challenging and timely issue for the 3rd Generation Partnership Project (3GPP) standardized low power wide-area network technology named narrowband Internet of Things (NB-IoT). Channel estimation is crucial to achieve extended radio coverage, energy efficiency, coherent detection, and channel equalization for signal repetition dominated NB-IoT uplink transmission. The NB-IoT inherits simplified baseband radio frequency processing, physical channels, reference signal structure, and numerology from existing Long Term Evolution (LTE) systems to save power and costs. Thus, channel estimation methods extensively employed in LTE systems may not be applied to the NB-IoT uplink systems. In this paper, efficient discrete cosine transform type-I (DCT-I)-based transform-domain channel estimation approaches are proposed by modifying the original definition of DCT-I. The proposed methods can mitigate the problems experienced in the discrete Fourier transform (DFT)-based channel estimation, such as signal aliasing error, and border effect. The proposed approaches improve channel estimation precision by reducing signal distortion from the high-frequency region in the time-domain when non-sample-spaced path delays exist in multipath fading channels. Signal aliasing error experienced from the virtual subcarriers is also minimized with the anticipated schemes. The proposed methods are applied on simple least squares (LS) estimates in time-domain to eliminate estimation noise. The viability of the proposed estimators is verified as compared to the conventional LS, DFT-based de-noising LS, and standard DCT-I based methods through extensive numerical simulations. Based on the numerical simulations, the proposed estimators show better mean square error and bit error rate performances than their competitors in extremely low coverage conditions.

Development and analysis of downlink scheduling algorithm in LTE system with imperfect channel quality indicator

Long-term evolution (LTE) is the broadband technology, introduced by third generation partnership project (3GPP) to support variety of multimedia services. Scheduling in LTE plays an important role to meet the system performance targets. In this paper, a new scheduling method is proposed, which considers the achievable rate based on the estimated channel condition of user in the priority metric calculation. It is highly uncertain to have perfect channel quality indicator (CQI) report at scheduler end due to poor quality of the channel and the variation of current channel condition with that of received CQI report. So, the channel condition of the user is ascertained with imperfect CQI, where the Kalman filter is used to estimate the channel condition and effectively recover the correct CQI from imperfect CQI to improve the system performance. The proposed scheduling provides better performance in terms of throughput, delay and packet loss rate (PLR) when compared with the already proposed service based scheduler (SBS) method.

Human Exposure to 4G LTE Systems: Enhancing the Reliability of EMF Extrapolation Techniques Based on Spectrum Analyzer Measurements

Radio frequency sources heavily impact human life due to the high number of technologies exploiting them. Their level assessment is a complex task because of differences in technologies working principles and high growth rate. Among them, cellular communications are the most pervasive, and several concerns are often addressed to currently deploying 5G. Here, 4G long-term evolution (LTE) technology is focused because it is still the largest used communication facility. To assess human exposure due to LTE, apart from traditional broadband and narrowband methods, easier and faster approaches have been proposed in the literature and the most recent technical standards: the extrapolation techniques (ETs). They measure pilot signals’ levels and process them to obtain the equivalent worst case channel power. They are designed to overestimate the current channel power by estimating the maximum channel power in the measurement point, thus warranting a conservative approach. Nevertheless, some of the needed hypotheses, such as the pilot signal power constancy, do not always pass the experimental validation. Based on that, in some cases, supposed overestimation can turn into underestimation, thus losing the “conservative” feature and making the power measurement unreliable. Through a wide experimental analysis, this article aims to highlight those issues and derive an improved measurement procedure, under the assumption that a basic spectrum analyzer is adopted as a measuring instrument.

Effects of bidirectional resource allocation schemes for advanced long-term evolution system in heterogeneous networks

Effects of bidirectional resource allocation schemes for advanced long-term evolution system in heterogeneous networks

The impact of PSS autocorrelation on cell search based on robust maximum likelihood scheme in LTE system

Robust Maximum Likelihood scheme for primary synchronization signal (PSS) detection and integer frequency offset (IFO) in Long Term Evolution (LTE) system has the best performance of cell search [5]. This scheme provides good synchronization performance based on correlation properties of Zadoff-Chu (ZC) sequences and reduced-rank representation of channel frequency response in multi-path propagation fading environment. The three ZC sequences used for PSS in LTE system cannot be said to have sufficient correlation properties in considering impact of symbol timing error. In this paper, we evaluate impact of the autocorrelation of PSS on PSS detection and IFO estimation and raise an issue of using other ZC sequence with better correlation properties. The simulation results show that PSS detection and IFO estimation performances are improved by using proposed ZC sequence.

Development and analysis of downlink scheduling algorithm in LTE system with imperfect channel quality indicator

Long-term evolution (LTE) is the broadband technology, introduced by third generation partnership project (3GPP) to support variety of multimedia services. Scheduling in LTE plays an important role to meet the system performance targets. In this paper, a new scheduling method is proposed, which considers the achievable rate based on the estimated channel condition of user in the priority metric calculation. It is highly uncertain to have perfect channel quality indicator (CQI) report at scheduler end due to poor quality of the channel and the variation of current channel condition with that of received CQI report. So, the channel condition of the user is ascertained with imperfect CQI, where the Kalman filter is used to estimate the channel condition and effectively recover the correct CQI from imperfect CQI to improve the system performance. The proposed scheduling provides better performance in terms of throughput, delay and packet loss rate (PLR) when compared with the already proposed service based scheduler (SBS) method.

Weak Radio-Frequency Signal Detection Based on Piezo-Opto-Electro-Mechanical System: Architecture Design and Sensitivity Prediction

We propose a novel radio-frequency (RF) receiving architecture based on the microelectromechanical system (MEMS) and optical coherent detection module. The architecture converts the received electrical signal into mechanical vibration through the piezoelectric effect and adopts an optical detection module to detect the mechanical vibration. We analyze the response function of the piezoelectric film to an RF signal, the noise-limited sensitivity of the optical detection module, and the system transfer function in the frequency domain. A simple on–off keying (OOK) modulation with carrier frequency 1 GHz is used to numerically evaluate the detection sensitivity. The result shows that considering the main noise sources in system, the signal detection sensitivity can reach around −116.6 dBm at bandwidth 5 MHz. Such sensitivity significantly outperforms that of the currently deployed long-term evolution (LTE) system. Finally, we conduct preliminary experiments based on an off-the-shelf aluminium nitride piezoelectric film, which shows that the vibration amplitude based on the theoretical prediction can be achieved.

Improved Delay Priority Resource Scheduling with Low Packet Loss Rate for MBMS in LTE Systems

An improved delay priority resource scheduling algorithm with low packet loss rate for multimedia broadcast multicast service (MBMS) in long term evolution (LTE) systems is proposed in this paper. Real-time services in LTE systems require lower delay and packet loss rate. However, it is difficult to meet the QoS requirements of real-time services using the current MBMS resource scheduling algorithm. The proposed algorithm in this paper jointly considers user delay information and real-time channel conditions. By introducing the user delay information, the lower delay and fairness of users are guaranteed. Meanwhile, by considering the channel conditions of users, the packet loss rate can be effectively reduced, improving the system throughput. Simulation results show that under the premise of ensuring the delay requirements of real-time services, the proposed algorithm achieves a lower packet loss rate compared to other existing algorithms. Furthermore, it can achieve a good balance between system throughput and user fairness.