High-speed Railway Communication Research Articles

Thermal stress simulation analysis of aerospace optical fibers and connectors and related extensions to high-speed railway area

Aerospace optical cables and fiber-optic connectors have numerous advantages (e.g., low loss, wide transmission frequency band, large capacity, light weight, and excellent resistance to electromagnetic interference). They can achieve optical communication interconnections and high-speed bidirectional data transmission between optical terminals and photodetectors in space, ensuring the stability and reliability of data transmission during spacecraft operations in orbit. They have become essential components in high-speed networking and optically interconnected communications for spacecrafts. Thermal stress simulation analysis is important for evaluating the temperature stress concentration phenomenon resulting from temperature fluctuations, temperature gradients, and other factors in aerospace optical cables and connectors under the combined effects of extreme temperatures and vacuum environments. Considering this, advanced optical communication technology has been widely used in high-speed railway communication networks to transmit safe, stable and reliable signals, as high-speed railway optical communication in special areas with extreme climates, such as cold and high-temperature regions, requires high-reliability optical cables and connectors. Therefore, based on the finite element method, comprehensive comparisons were made between the thermal distributions of aerospace optical cables and J599III fiber optic connectors under different conditions, providing a theoretical basis for evaluating the performance of aerospace optical cables and connectors in space environments and meanwhile building a technical foundation for potential optical communication applications in the field of high-speed railways.

Wireless Perception of High-Speed Railway Communication: Challenges, Framework, and Future Directions

Wireless Perception of High-Speed Railway Communication: Challenges, Framework, and Future Directions

Location and History Information Aided Efficient Initial Access Scheme for High-Speed Railway Communications

Location and History Information Aided Efficient Initial Access Scheme for High-Speed Railway Communications

User-Assisted Base Station Caching and Cooperative Prefetching for High-Speed Railway Systems

With the aim of reducing the transmission latency, this paper proposes a scheme of user-assisted base station (BS) caching and cooperative prefetching for high-speed railway (HSR) communications. In the model under consideration, neighboring BSs periodically exchange information, including the coverage area and communication rate, to facilitate content caching and prefetching. As an additional means, users can cache contents that are different from BSs. Specifically, we construct an optimization problem for content caching and prefetching that minimizes the overall transmission latency. Moreover, this problem is decomposed into two subproblems, namely the one aiming to maximize the available throughput of BSs and the other attempting to maximize the user cache utilization subject to the constraint of the BS cache. We demonstrate that the objective function of the first subproblem is equivalent to a monotone submodular function subject to matroid constraints. Therefore, it can be efficiently solved with a greedy algorithm. Meanwhile, the user cache issue can be viewed as a weighted sum maximization problem. Simulation results are presented to verify that the proposed scheme can not only reduce the average transmission latency but also improve the hit probability and system throughput.

Performance and Capacity Optimization for High Speed Railway Communications Using UAV-IRS Assisted Massive MIMO System

In this paper, we study the communication performance of applying unmanned aerial vehicles (UAVs) combined with intelligent reflective surfaces (IRS) in a high speed railway (HSR) scenario. This study investigates the design and performance of (multiple-input-multiple-output) MIMO systems with UAV and IRS assistance technology in high-mobility scenarios. Direct links between base stations (BS) and trains are often obstructed in suburban environments, especially in mountainous areas. We mount the IRS on the UAVs so that it can assist in the communication between the trains and the BS. With the help of the UAV-IRS, straight-line links can be established effectively, which greatly improves communication for train passengers. This paper considers the employment of large-scale antenna arrays at both the BS and train ends. Train passengers communicate with UAVs via antennas assembled on the roof of the train as gateways, which in turn communicate with the BS. We consider two types of antenna layouts on the train: all antennas are located in the center of the train named Co-located antennas (CA) layout and uniformly distributed along the train called distributed antennas (DA) layout. We can obtain the analytical up-link capacity by averaging over all locations in a cell for the above two layouts by considering the radio frequency consumption. Overall, the CA layout is found to be a better option for trains when attempting to maximize cell mean value of capacity, and DA layout achieves a more uniformly distribution of capacity over the entire cell. Ultimately, the best solution will depend on the specific requirements and constraints of the selected deployment scenario.

Joint Design of Phase Shift and Transceiver Beamforming for Intelligent Reflecting Surface Assisted Millimeter-Wave High-Speed Railway Communications

Joint Design of Phase Shift and Transceiver Beamforming for Intelligent Reflecting Surface Assisted Millimeter-Wave High-Speed Railway Communications

A Multifrequency Seamless Dual-Link Handover Scheme Based on Beamforming for High-Speed Railway

This paper proposes a multifrequency seamless dual-link handover scheme based on beamforming for the high-speed railway (HSR) communication, i.e., when the train travels to the edge of overlapping area of the adjacent two cells, a gain beam is assigned to the overlapping area by the target base station (BS) using a beamforming technique to cover the entire region to enhance the received signal strength (RSS) and handover opportunity. The switching antenna is allowed to handover multiple times to improve the traditional scheme and reduce the handover failure probability (HFP) greatly. In the process of signal reception, considering the impact of the intercell cochannel interference on the RSS, the proposed scheme uses signal to interference ratio (SIR) instead of the RSS to indicate the received signal quality to optimize handover mode. The handover success probability (HSP) is analyzed to describe the relationship between train location and the probability. We also establish a probabilistic model and corresponding handover algorithm for the proposed scheme to complete the handover operations and theoretically analyze a series of indexes including handover trigger probability (HTP), HFP, communication interruption probability (CIP), and HSP. Theoretical and experimental results show that the proposed scheme can effectively improve the HTP and HSP and greatly reduce the HFP and the CIP.

RIS-Aided Hybrid Precoder Design With Anti-Blocking Scheme in High-Speed Railway

Reconfigurable intelligent surface (RIS)-aided high-speed railway (HSR) communication system is an efficient and promising way to slove the signal blocking by tuning passive signal reflection or refraction. However, the complexity HSR environment lead unstable communication links and low quality of service. Therefore, this paper studies a RIS-aided hybrid precoding for anti-blocking HSR network to enhance the sum rate of mobile relays when signal blocking. To describe the blocking scenes clearly, the blocking model is established and the closed form of the blocking probability is derived. Based on the blocking model, the non-convex blocking optimal problem is decoupled by the block coordinate descent method with the jointly optimization problem about hybrid precoder and reflect angle matrix of RIS. Then, the successive convex approximation and prox-linear method are utilized to solve the constant modulus constraint of RIS and hybrid precoding constraints. Simulation results show that the proposed RIS-aided anti-blocking hybrid precoding scheme can achieve higher sum rate than the traditional methods in blockage scenarios. Moreover, the proposed algorithm brings better performance of blocking probability and outage probability in high-speed scenarios.

Precoding-Based Handover Scheme Design for High-Speed Railway Communication

In high-speed railway (HSR) communication, the handover becomes more frequent for the high mobility of trains. In this letter, a precoding based handover scheme is proposed to reduce the ping-pong handover trigger rate and improve the HSR system stability. Specifically, the precoding vectors of serving and target cell are jointly designed to maximize the difference in received signal strength between the two cells during the handover preparation phase. And the optimal precoding vectors is solved through the concave-convex process and the second-order cone programming transformation. The simulation results show that the proposed handover scheme can effectively alleviate the occurrence of ping-pong handover and ensure the system stability of HSR communication. The system has higher signal strength than with the traditional scheme.

MmWave extra-large-scale MIMO based active user detection and channel estimation for high-speed railway communications

The current High-Speed Railway (HSR) communications increasingly fail to satisfy the massive access services of numerous user equipment brought by the increasing number of people traveling by HSRs. To this end, this paper investigates millimeter-Wave (mmWave) extra-large scale (XL)-MIMO-based massive Internet-of-Things (IoT) access in near-field HSR communications, and proposes a block simultaneous orthogonal matching pursuit (B-SOMP)-based Active User Detection (AUD) and Channel Estimation (CE) scheme by exploiting the spatial block sparsity of the XL-MIMO-based massive access channels. Specifically, we first model the uplink mmWave XL-MIMO channels, which exhibit the near-field propagation characteristics of electromagnetic signals and the spatial non-stationarity of mmWave XL-MIMO arrays. By exploiting the spatial block sparsity and common frequency-domain sparsity pattern of massive access channels, the joint AUD and CE problem can be then formulated as a Multiple Measurement Vectors Compressive Sensing (MMV-CS) problem. Based on the designed sensing matrix, a B-SOMP algorithm is proposed to achieve joint AUD and CE. Finally, simulation results show that the proposed solution can obtain a better AUD and CE performance than the conventional CS-based scheme for massive IoT access in near-field HSR communications.

Weighted Score Fusion Based LSTM Model for High-Speed Railway Propagation Scenario Identification

Propagation scenario identification is of vital significance for boosting the performance of future smart high-speed railway (HSR) communication networks. This paper investigates the HSR propagation scenario identification model, based on deep learning networks and feature fusion methods. With the assistance of railway long-term evolution (LTE) networks, we collected the channel impulse responses in four typical HSR scenarios including unobstructed viaduct, obstructed viaduct, station and suburban. Four channel characteristics involving power delay profile, root mean square (RMS) delay spread, RMS angular spread and Ricean K-factor form the datasets used for model training and testing. Then, a novel propagation scenario identification model is proposed by merging a weighted score based feature fusion method into the long short-term memory (LSTM) neural network. The hyper-parameters of the proposed model such as time window length and numbers of hidden units and layers are determined by autocorrelation analysis and cross-validation. Finally, the model performance is evaluated by focusing on the impact of feature selection, comparison of different feature fusion methods, and computational complexity. The evaluation results show that the proposed model has high identification accuracy but acceptable computational complexity.

Coverage Analysis for Cell-Free Massive MIMO High-Speed Railway Communication Systems

In traditional cellular framework, the frequent handoff is one of the main factors that affect the performance of high-speed railway (HSR) communication systems. The cell-free massive multiple input multiple output (MIMO) is proposed as a promising technique for the antenna point (AP) deployment of HSR communication systems. This paper investigates the coverage performance of the cell-free massive MIMO HSR communication systems with multiple APs and multiple users. Initially, the system and channel models are proposed. Based on the established models, the uplink training, uplink data transmission and downlink data transmission are analyzed, respectively. By using the deterministic equivalent analysis, we derive a deterministic signal-to-interference-plus-noise ratio expression for downlink transmission. Then, a tight upper bound on the coverage probability is derived. In the high power regime, the asymptotic coverage performance is also analyzed. Numerical results verify the correctness of the derived theoretical expressions. Moreover, the effects of the downlink transmit power, AP’s antenna number, AP’s density, user number, and channel fading parameter on coverage performance are also discussed.

KF-LSTM Based Beam Tracking for UAV-Assisted mmWave HSR Wireless Networks

Owing to the deployment flexibility, unmanned aerial vehicles (UAVs), equipped with lightweight base stations (BSs), provide a new paradigm for wireless access from the sky to temporarily complement terrestrial networks, which bring benefits to many applications, such high-speed railway (HSR) communications. In UAV-assisted wireless communications, the advantage of the line of sight (LOS) dominant air-ground channels makes it more attractive to apply directional millimeter wave (mmWave) communications, where beam management is highly related to user location. Nevertheless, due to the high mobility of UAVs and trains, how to accurately and efficiently track beams becomes a critical problem to solve. Based on this observation, in this paper, we first present an UAV-assisted dual-band HSR wireless network architecture, which integrates the frequency bands below 6 GHz (sub-6 GHz) to provide reliable transmissions for UAV controls and HSR safety services, with the mmWave bands to enhance the transmission capacity between UAVs and trains. To solve the beam tracking problem, we analyze the beam angular variations, and then propose an efficient algorithm based on Kalman filtering (KF) with varying update periods and long-short term memory (LSTM) to improve the beam tracking performance. Finally, we conduct extensive simulations to demonstrate that the proposed scheme can achieve more accurate beam tracking and higher spectrum efficiency.

Neural Network-Based Multi-DOA Tracking for High Speed Railway Communication Systems

Along with the rapid development of high-speed railway (HSR), it is of vital importance to explore new technologies to meet the stringent requirements on wireless transmission latency and reliability in such high mobility scenarios. To address this, fast beamforming schemes with precise direction-of-arrival (DOA) can achieve effective channel gain and improve interference mitigation. In this paper, we propose a novel radial-basis function neural network (RBFNN)-based method for the multi-DOA tracking, in which the RBFNN is trained by the location-based prior information. Consequently, the DOA tracking problem is transformed into an RBFNN with trained input/output pairs. The effectiveness of the proposed scheme in terms of complexity and accuracy is verified by simulation results. Finally, the impact of the shapes of the array on estimation accuracy is also discussed.

Coverage Analysis and Chance-Constrained Optimization for HSR Communications With Carrier Aggregation

This paper investigates coverage analysis and performance optimization for a high-speed railway (HSR) communication system with carrier aggregation (CA). Initially, the system model is established. By using CA technology, the statistical characteristic of the received signal-to-noise ratio (SNR) is obtained. Then, for the systems with or without CA, unified theoretical expressions for the edge coverage probability (ECP) and the percentage of cell coverage area (CCA) are obtained, respectively. After that, a chance-constrained coverage optimization is proposed to improve the coverage performance, which is effectively solved by using a heuristic algorithm. Numerical results show that the derived expressions of the ECP and the percentage of CCA are accurate and can be directly used to evaluate the coverage performance without time-intensive simulations. Moreover, the impacts of CA, transmit power, cell radius, received SNR threshold, HSR scenario on coverage performance have been discussed. Furthermore, the effectiveness of the proposed coverage optimization scheme is also verified.

Research on High-Speed Railway Communication Strategy Based on LIFI Technology

In order to improve the effect of high-speed railway communication, this paper studies the high-speed railway communication strategy combined with LIFI technology and improves the effect of high-speed railway communication through intelligent communication methods. Moreover, this paper studies the access point allocation problem in indoor LiFi/WiFi hybrid network, fully considers the dynamic change of network load, and introduces a dynamic load balancing algorithm that determines AP allocation in hybrid network based on the data transmission rate as the switching threshold. In addition, this paper proposes two AP allocation methods, such as dynamic switching threshold and minimum data rate constraint, to construct a reliable high-speed railway communication model. The experimental results verify that the high-speed railway communication strategy based on LIFI technology proposed in this paper can effectively improve the high-speed railway communication effect.

Double IRSs Aided Massive MIMO Channel Estimation and Spectrum Efficiency Maximization for High-Speed Railway Communications

Intelligent reflecting surface (IRS)-enhanced wireless communication is one of the critical technologies of the sixth generation mobile communication, which has recently emerged as an innovative solution to the realization of intelligent transportation. It can meet the increasing demand for high-speed railway (HSR) communications services. In this paper, we consider a train-ground TDD wireless mobile communication paradigm to deploy two IRSs on the roadside and the train roof to assist the wireless transmission for the HSR communication system. To achieve the more significant performance gain, in the uplink transmission phase, we propose the practical channel estimation framework of the MIMO system aided by double IRSs to acquire reliable channel state information (CSI) of each channel. To facilitate the processing of the three-dimension tensor, we leverage the trilinearity of the parallel factor decomposition to decompose the received signal into a two-dimensional matrix. The Doppler frequency offset compensating scheme and channel tracking scheme based on Kalman-Filter are proposed to further decrease the pilot overheads. Then, in the downlink phase, a block coordinate descent (BCD) algorithm is proposed to handle the data rate maximization problem of double IRSs communication system with the estimated CSI. We reformulate the original problem and decouple the double IRSs phase optimization problem into two single IRS phase optimization non-convex problems under the unit-modulus constraint. The Majorization-Minimization (MM) algorithm is proposed to approximate the optimal solution of the two optimization subproblems. Simulation results corroborate the effectiveness of the proposed channel estimation and phase shifts optimization scheme.

Scheduling of UAV-Assisted Millimeter Wave Communications for High-Speed Railway

To exploit richer spectrum resources for even better service quality, millimeter wave (mmWave) communication has been considered for high-speed railway (HSR) communication systems. In this paper, we focus on scheduling as many flows as possible while satisfying their QoS requirements. Due to interference, eavesdropping, or other problems, some flows may not be directly transmitted from the track-side BS. In this paper, we propose an UAV-assisted scheduling scheme which utilizes a UAV to serve as relay for such flows. The proposed scheme also utilize two mmWave bands, one for the BS links and the other for the UAV links. The proposed algorithm aims to maximize the number of flows with their QoS requirements satisfied. Simulations demonstrate that the proposed scheme achieves a superior performance on the number of completed flows and the system throughput over two baseline schemes.

Deep-Learning-Based Spatial–Temporal Channel Prediction for Smart High-Speed Railway Communication Networks

Intelligent channel prediction plays a key role in artificial intelligence (AI)-optimized or AI-native communication networks for smart high-speed railways (HSRs). This paper investigates the spatial-temporal prediction of channel state information (CSI) and channel statistical characteristics (CSCs) based on deep-learning (DL) for the future smart HSR communication network. A propagation-graph simulation method is used to generate datasets of CSI and CSCs for massive multiple-input multiple-output (mMIMO) channels in a HSR cutting scenario, and realistic channel measurements are used to validate the datasets. Then, single-step ahead and multi-step ahead prediction problems are formulated with the consideration of both spatial and temporal information hidden in the datasets. By exploiting the temporal and spatial correlations of the HSR mMIMO channel, a novel spatial-temporal channel prediction model that combines the convolutional neural network (CNN) and convolutional long short-term memory (CLSTM) is proposed and called as Conv-CLSTM. Moreover, the hyper-parameters of the Conv-CLSTM model are determined by autocorrelation and similarity analysis and cross-validation. Finally, the performance of the Conv-CLSTM model is evaluated in terms of prediction accuracy and space and time computational complexity, and is compared with classical DL models. The evaluation results show that the proposed model has high prediction accuracy but acceptable computational complexity.

Low-Band Information and Historical Data Aided Non-Uniform Millimeter Wave Beam Selection Algorithm in 5G-R High-Speed Railway Communication Scene

Low-Band Information and Historical Data Aided Non-Uniform Millimeter Wave Beam Selection Algorithm in 5G-R High-Speed Railway Communication Scene