Long Term Evolution For Railway Research Articles

A Fast Prediction Method for the Electromagnetic Response of the LTE-R System Based on a PSO-BP Cascade Neural Network Model

In this paper, a fast prediction model of the electromagnetic response of the LTE-R (Long Term Evolution for Railway) communication system based on a cascade neural network is developed to quickly analyze the impact of pantograph arcing on the LTE-R system during vehicle operation. In order to obtain the coupling disturbance level of the LTE-R antenna port, this model uses the cascade neural network based on the PSO-BP (Particle Swarm Optimization of Back Propagation Neural Network) algorithm to quickly solve the coupling coefficient of the pantograph arcing measurement probe and the antenna port. A two-stage BP neural network model is used to train both the simulation data and measurement data, and the results are validated by field tests. Finally, the antenna port coupling interference is added to the LTE-R system to analyze the impact of pantograph arcing on the quality of communication transmission. Analysis and tests demonstrate that pantograph arcing can lead to an increase in the BLER of LTE-R systems at 400 MHz, affecting system performance and transmission efficiency.

Joint fast time domain channel estimation with ICI cancellation for LTE-R systems

In this paper, we consider the high-speed railway (HSR) communication bases on the long-term evolution for railway (LTE-R) platform. Since the large Doppler spread is introduced due to the movement of the train, the orthogonality of subcarriers is destroyed resulting in the inter-carrier interference (ICI). Moreover, the critical speed of transceivers moving leads to a very fast-time varying channel within an OFDM symbol, hence the existing frequency domain channel estimation (FDCE) method cannot reliably estimate the channel state information (CSI). To better track the fast-time varying channel, we propose a new framework for LTE-R systems. First, we modify the WINNER II channel model and the D2a propagation scenario to approximate the multipath fading and high Doppler spread in high mobility scenarios. Second, we use a novel pilot structure-based time domain channel estimation (TDCE), helping to track the channel variations for each channel path separately. The CSI at data subcarriers is estimated by using different conventional interpolation methods to reconstruct the ICI channel matrix in the frequency domain. Different from the linear channel model, the channel time-variations of current OFDM data symbol are predicted from the third-order polynomial function, which is reconstructed base on CIR at the pilot position by using several constitutive OFDM symbols. The cubic Hermite function is chosen by considering its simplicity and efficiency. Lastly, we propose the deep neural network (DNN) based CE in LTE-R systems, which helps to further improve the accuracy of estimated channel information in the previous state, especially at low signal-to-noise (SNR) level. The simulation results show that our proposed CE method for each channel path agrees well with the theoretical derivation and the simulation. The system performance with the proposed framework is significantly improved in comparison with state-of-the-art methods.

Improving the Security of LTE-R for High-Speed Railway: From the Access Authentication View

Security and efficiency are crucial considerations for Long Term Evolution for Railway (LTE-R) which bears real-time transmission of train control information for future high-speed railway. In this article, we first analyze the vulnerabilities of LTE-R access authentication protocol, and then propose a proxy signature based authentication scheme to enhance the security of LTE-R without sacrificing efficiency. The proposed scheme consists of three main security mechanisms: a novel Elliptic Curve Cryptosystem based Certificateless Proxy Signature (ECC-CLPS) designed for authentication security, a hash-based puzzle introduced to defend against denial of service (DoS) attack and a key pre-generation mechanism used to improve the efficiency of fast handover authentication. The security analysis and performance simulation show that our scheme has advantages over existing LTE-based authentication schemes in terms of security, functionality, computation and communication costs. Moreover, the authentication requirements for security and efficiency in different LTE-R communication scenarios are fully considered, which makes our scheme more suitable for the access authentication in the future LTE-R based high-speed railway.

Parameter Adaptation and Situation Awareness of LTE-R Handover for High-Speed Railway Communication

In the evolution of railway mobile communications from Long Term Evolution for Railway ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LTE-R</i> ) to the future 5th Generation Wireless System ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5G</i> ), the rapid increase in the number of low-power base station nodes along the railway has brought more frequent handovers. The current handover parameter selection mechanism often relies on the on-site measured results in a limited number of discrete scenarios. It cannot deal with the continuous changing characteristics of the high-speed railway mobile communication environment, which leads to a serious lack of accuracy, adaptability and intelligence. This article hopes to construct a parameter-adaptive handover mechanism suitable for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5G</i> in the high-speed railway dedicated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LTE-R</i> communication system. The mechanism first uses the interaction of Temporal-Difference( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TD</i> )-learning-based reinforced agents to obtain high-speed railway handover performance and network performance in different combinations of speeds and handover parameters, and continuously updates the accumulated rewards used to target optimization, obtaining a Discrete TD value cube with closely related handover performance. Further, based on the Discrete TD value cube, we use the approximation function method for the completion of “continuous” situation of handover parameter selection, and construct a continuous TD value cube and the corresponding performance cubes. Our experimental results prove that TD learning agents with function approximation can accurately estimate and predict the handover performance and network performance of state combinations with different speeds and handover parameters, and further show that the handover parameter adaptation mechanism based on the Inference ability can find the optimal handover parameters to improve the handover performance and network performance.

Adaptive modulation and frame length method based on Moore state machine in LTE‐R communication system

In this study, the problem of adaptive modulation and frame length (AMFL) is investigated in long-term evolution for railway (LTE-R) communication system. By considering the effect of frame length, a novel AMFL strategy based on Moore state machine (MSM) is proposed. The influence of the frame length on the goodput is discussed by theoretical analysis and simulation evaluation. According to modulation and frame length schemes (MFLSs) adopted in LTE-R system, the finite state set of the MSM is designed. For different MFLSs, the frame error rate (FER) and signal-to-noise ratio (SNR) curve is obtained. By giving a target FER, the SNR thresholds corresponding to different MFLSs are achieved. From the perspectives of modulation priority and frame length priority, two different AMFL-MSMs are designed to change MFLSs dynamically. Simulation results show that the proposed two AMFL-MSMs have more stable goodput than the common modulation schemes and less symbol energy consumption than the high-order modulation scheme. In addition, the AMFL-MSM with modulation priority has better performance than the AMFL-MSM with frame length priority in terms of goodput stability.

Temporal-Spatial Collaborative Prediction for LTE-R Communication Quality Based on Deep Learning

In recent years, long term evolution for railway (LTE-R) has been a promising technology to meet the growing demand for railway wireless communication. To realize the active maintenance of LTE-R base station, it is of great significance to precisely predict the communication quality (CQ) of LTE-R base station. Given that the existing LTE CQ prediction methods can not support the active maintenance of LTE-R base station. Furthermore, the LTE-R base station has its unique characteristics in time relationship and regional impact, one of the most challenging problems is to effectively integrate the temporal and spatial information to improve the effect of CQ prediction. To solve the above problems, we choose daily evolved radio access bearer (E-RAB) abnormal release ratio as the CQ indicator, and propose a new deep learning-based CQ prediction approach for LTE-R. Considering the influence of adjacent base stations, this method conducts temporal-spatial collaborative prediction on multivariate time series collected from the CQ data of these stations. First, to eliminate the negative effect of redundant variables, a new variable filter method based on max-relevance, and min-redundancy (MRMR) criterion and binary particle swarm optimization (BPSO) is proposed to select a variable set from the CQ data of related base stations. Second, a new recurrent convolutional neural network (RCNN) model with a self-attention mechanism is proposed to extract temporal-spatial features from the selected variable set. With these features, we build a collaborative prediction model for CQ prediction. Experimental results on real-world LTE-R CQ datasets demonstrate the superiority of the proposed method in CQ prediction.

When High-Speed Railway Networks Meet Multipath TCP: Supporting Dependable Communications

Multipath TCP (MPTCP) is an extraordinarily promising technology to guarantee service dependability in high-speed railway (HSR) networks. However, the phenomenon that wireless losses caused by frequent handoffs or other non-congestion losses in HSR networks has a detrimental influence on the performance of MPTCP. In order to improve performance of MPTCP when Long-Term Evolution for Railway (LTE-R) and WiFi communication routes are aggregated in HSR networks, we present a novel congestion control scheme, i.e., RVeno in this letter. Simulation results clearly reveal RVeno outperform the existing MPTCP congestion control algorithms in terms of throughput.

An Efficient MIMO Channel Model for LTE-R Network in High-Speed Train Environment

Long-term evolution for railway (LTE-R) represents a promising network for smart railway, enabling fast and reliable wireless communications on high-speed trains (HSTs) with speeds as high as 500 km/h. To the best of our knowledge, the existing channel models are insufficient to assess such performance. To fill this gap, we propose the tapped delay line model for a $2\times 2$ multiple-input multiple-output channel in typical LTE-R HST scenarios. The models are efficient, yet flexible, which can capture the essential channel characteristics in the delay domain, Doppler domain, and spatial domain. Due to the practical difficulties of HST channel measurements, the channel between the fixed transmitter and the high-mobile train cannot be recorded sufficiently for establishing these stochastic models. Therefore, we develop channel models using a ray-tracing (RT) simulator that is validated by HST channel measurements at 2.15 GHz. This paper gives a full parameterization for the proposed channel models, and presents their implementation in channel and link-level simulations. The verification is made through a comparison of the proposed models and the measurement-validated RT simulations in terms of the delay spreads, the eigenvalues, and the error vector magnitudes.

Channel Modeling for Future High-Speed Railway Communication Systems: A Survey

The widespread popularity of high-speed railways (HSRs) urges a critical demand on high-data-rate railway communication services for both train operation and passenger experience. To satisfy the ever-increasing requirements, future HSR communication systems, such as long-term evolution for railway (LTE-R), fifth generation (5G) on HSR, and 5G for railway (5G-R), and corresponding transmission technologies, e.g., mobile relay, coordinated multipoint, massive multiple-input multiple-output (MIMO), and millimeter-wave (mmWave), have recently attracted much attention. Radio channel modeling is the foundation of design and evaluation of wireless systems and transmission technologies. This paper focuses on a survey of channel modeling for the future HSR communication systems. The significant requirements of future HSR channel models are highlighted, and recent advances in the HSR channel modeling are reviewed. Finally, potential research directions for future HSR channel modeling are outlined.

High mobility channel estimation method based on improved basis expansion model

In this paper, the problem of high mobility channel estimation in the Long-Term Evolution for Railway (LTE-R) communication system is investigated. By using a Basis Expansion Model (BEM), the channel impulse response is modeled as the sum of several basis functions multiplied by coefficients. By estimating the basis function coefficients, the fast time-varying channel can be approximated. In order to reduce the estimation error resulting from the high frequency basis function, the Generalized Complex Exponential BEM (GCE-BEM) is modified to form an Improved GCE-BEM (IGCE-BEM) by adding a correction coefficient to the basis function. Moreover, an Improved Baseline Tilting (IBT) method is proposed to reduce the Gibbs effect. In addition, linear interpolation, Gauss interpolation, and three-order Hermite interpolation are adopted to obtain the channel impulse response at non pilot locations based on the channel estimation results at pilot positions. The simulation results show that the IGCE-BEM outperforms the CE-BEM and GCE-BEM in terms of the Normalized Mean Squared Error (NMSE). The IBT method is better than the BT method in reducing the Gibbs effect. In addition, combined with the IBT, the IGCE-BEM also has low NMSE under high moving speed and high noise power. The performance of the three-order Hermite interpolation method is higher than that of the linear interpolation and Gauss interpolation approaches.

Joint Estimation of MCFOs and Channel Gains for Two-Way Multi-Relay Systems With High Mobility

This letter proposes space-alternating generalized expectation maximization and expectation conditional maximization-based joint estimation of multiple carrier frequency offsets and channel gains for two-way relay network with multiple relays. The method iteratively updates least square estimates with less time and stable convergence. We adopt discrete prolate spheroidal and Karhunen-Loeve basis expansion models to capture rapid time variations of channel. Proposed estimators result in acceptable performance for very high user mobility (corresponds to long term evolution for railway). The derived Cramer-Rao lower bound is shown to be comparable with MSE performances.

Quality of service improvement for high-speed railway communications

With the fast development of highspeed railways, a call for fulfilling the notion of communication at “anytime, anywhere” for high-speed train passengers in the Train Operating Control System is on the way. In order to make a realization of that, new railway wireless communication networks are needed. The most promising one is the Long Term Evolution for Railway which will provide broadband access, fast handover, and reliable communication for high mobility users. However, with the increase of speed, the system is subjected to high bit error rate, Doppler frequency shift and handover failure just like other system does. This paper is trying to solve these problems by employing MIMO technique. Specifically, the goal is to provide higher data rate, higher reliability, less delay, and other relative quality of services for passengers. MIMO performance analysis, resource allocation, and access control for handover and various services in a two-hop model are proposed in this paper. Analytical results and simulation results show that the proposed model and schemes perform well in improving the system performances.

Efficient Service Delivery for High-speed Railway Communications Using MIMO and Access Control Technique

In order to realize the notion of “anytime, anywhere” communication for high-speed train passengers, the Long Term Evolution for Railway is designed to provide broadband accesses and reliable communications for high-speed train passengers. However, with the increase of train speed, the system is subject to high bit error rate, Doppler frequency shift, and call drops. This paper is trying to solve these problems by employing the Multiple-Input Multiple-Output technique and access control schemes. The goal is to provide higher quality of services such as data rate, reliability, and delay for train passengers. Physical layer performance analysis and access control schemes are proposed in a two-hop model. Handovers and service types are also considered. Simulation results show that proposed models and schemes perform better in improving the quality of services.

Beamforming and positioning-assisted handover scheme for long-term evolution system in high-speed railway

As the railway industry expands and trains speed-up, the reliability and efficiency of wireless communication systems in high-speed railway have to face the severe challenges. The high-speed brings about more severe Doppler effect, the postponement of handover triggering location and more frequent handover. Therefore the influence caused by the high-speed decreases the reliability of wireless communication network. In this study, considering the decision of `global system for mobile communication for railway` evolution to long-term evolution for railway (LTE-R) by International Union of Railways, a scheme based on the beamforming and positioning information is proposed to improve the system handover performance for the LTE system which is being applied to the high-speed railway. In the overlapping region, both the current serving eNodeB and the target eNodeB start to use beamforming with different gain factors to improve handover success probability. The results have shown that the proposed scheme increases the handover success probability effectively.