High-speed Railway Communication Research Articles

Double-RISs-Aided Predictable High-Speed Railway Communications With Doppler Effect Mitigation

Double-RISs-Aided Predictable High-Speed Railway Communications With Doppler Effect Mitigation

Antenna Deployment Optimization for Coverage Performance Improvement in High-Speed Railway Communications

Antenna Deployment Optimization for Coverage Performance Improvement in High-Speed Railway Communications

A novel method for reducing the effect of doppler frequency shift in high-speed railway mobile communication using machine learning

Currently, high-speed railway systems are rapidly expanding worldwide, necessitating reliable and efficient mobile communication solutions. However, the Doppler frequency shift caused by the high speeds of trains presents significant challenges to communication systems, particularly those using OFDM in LTE. This paper presents a novel for Doppler frequency compensation in high-speed railway communication based on the results of estimating the train's velocity using machine learning algorithms. By leveraging advanced algorithm such as neural networks, our method dynamically predicts and compensates for Doppler shifts in real-time. In this proposed novel, the Doppler frequency value is calculated based on the actual train’s velocity and the scenario of a high-speed railway, then the Doppler frequency is used to compensate the carrier frequency offset (CFO) directly at the Access Point (AP) device on the train. We conduct simulations to evaluate the effectiveness of our proposed solution in a high-speed railway scenario. The results demonstrate a marked improvement in communication reliability and data integrity, highlighting the potential of machine learning to enhance the performance of mobile communication systems in high-speed railways. The results of the proposed model are evaluated based on the system’s bit error rate BER after the CFO compensation decreases and the ratio of average energy per bit (Eb) to noise power density (N0) (Eb/N0) increases. This shows that the proposed solution works effectively and reduces the system’s bit errors while improving the communication performance. This study opens new avenues for integrating intelligent systems in transportation networks, ensuring seamless connectivity, and improving passenger experience

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

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

Coverage Analysis for High-Speed Railway Communications with Narrow-Strip-Shaped Cells over Suzuki Fading Channels.

Unlike circular cell coverage in public land mobile communications, narrow-strip-shaped cell coverage should be considered in high-speed railway (HSR) communications. Moreover, for the coverage analysis in HSR communications, most works ignore the effect of small-scale fading, which results in an inaccurate coverage performance evaluation. In this paper, we focus on the coverage analysis for HSR communications with narrow-strip-shaped cells over the Suzuki fading channel, where the composite channel fading includes path loss, lognormal shadowing, and Rayleigh-distributed small-scale fading. Based on the channel model, we first analyze the statistical characteristic of the received signal-to-noise ratio. Then, we derive analytical expressions of the edge coverage probability (ECP) and the percentage of cell coverage area (CCA). To link the edge coverage performance and the average coverage performance of a cell, we express the percentage of CCA as a summation of the ECP and a positive increment. As special cases, we also obtain the coverage performance expressions for the systems without small-scale fading. Through Monte Carlo simulations, the accuracy of the derived expressions is verified. Numerical results also show that the small-scale fading has a strong effect on coverage performance and cannot be ignored. In addition, the effects of key parameters are also discussed.

Optimization of resource allocation strategy for high-speed railway based on deep reinforcement learning

With the accelerated development of high-speed railway (HSR), the contradiction between the surge of user services and the demand for resource has become increasingly prominent. Mobile edge computing (MEC) has emerged to improve performance, reduce communication delay and ease network load. In this paper, we design a multi-user MEC system framework that aims to solve the joint optimization problem of computation offloading and resource allocation in HSR communication scenario with deep reinforcement learning algorithm. The framework dynamically allocates computation resource and network bandwidth through the real-time distance between users and base station (BS) to achieve optimal resource utilization and maximize user experience. To achieve this goal, we use a deep reinforcement learning based dynamic computation offloading and resource allocation (DDCORA) optimization algorithm. The algorithm minimizes the system cost by sharing state information among different users and making collaborative decisions to rationally allocate spectrum resource and computation resource. Simulation results show that DDCORA algorithm can significantly decrease the system cost while enhancing the overall system performance and user experience.

A reconstruction and recovery network-based channel estimation in high-speed railway wireless communications

The integration of high-speed railway communication systems with 5G technology is widely recognized as a significant development. Due to the considerable mobility of trains and the complex nature of the environment, the wireless channel exhibits non-stationary characteristics and fast time-varying characteristics, which presents significant hurdles in terms of channel estimation. In addition, the use of massive MIMO technology in the context of 5G networks also leads to an increase in the complexity of estimation. To address the aforementioned issues, this paper presents a novel approach for channel estimation in high mobility scenarios using a reconstruction and recovery network. In this method, the time-frequency response of the channel is considered as a two-dimensional image. The Fast Super-Resolution Convolution Neural Network (FSRCNN) is used to first reconstruct channel images. Next, the Denoising Convolution Neural Network (DnCNN) is applied to reduce the channel noise and improve the accuracy of channel estimation. Simulation results show that the accuracy of the channel estimation model surpasses that of the standard channel estimation method, while also exhibiting reduced algorithmic complexity.

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.

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.