High-speed Railway Wireless Research Articles

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.

Directive Millimeter-Wave End-Fire Diplexing Antenna for IoT Applications

Millions of passengers use High-speed trains (HSTs) daily, in the world. The roofs of HSTs can be equipped with a variety of transmitting and receiving antennas to accommodate high-data-rate Internet services with reliable radio access for passengers. The design of commercial antenna systems is a compromise between cost, size, manufacturability, and performance. This paper proposes a new, compact and low-cost two-port millimeter-wave diplexing directive end-fire antenna, which is designed, fabricated and tested. The proposed antenna is made of a tapered slot antenna and a diplexing structure based on transitions in stacked Printed Circuit Boards (PCBs). The ports of the antenna cover the frequency band 25.9-32.4 GHz (6.5 GHz) and 34.6-37.1 GHz (2.5 GHz). In all operating frequency bands, measured isolation between input ports exceeds 24 dB. Furthermore, symmetric directive end-fire radiation patterns are obtained experimentally, with low side lobe level and low front-to-back ratio. All these features make the proposed diplexing antenna suitable for high-speed railway wireless networks.

Doppler Shift and Channel Estimation for Intelligent Transparent Surface Assisted Communication Systems on High-Speed Railways

The critical distinction between the emerging intelligent transparent surface (ITS) and intelligent reflection surface (IRS) is that the incident signals can penetrate the ITS instead of being reflected, which enables the ITS to combat the severe signal penetration loss for high-speed railway (HSR) wireless communications. This paper thus investigates the channel estimation problem for an ITS-assisted HSR network where the ITS is embedded into the carriage window. We first formulate the channels as functions of physical parameters, and thus transform the channel estimation into a parameter recovery problem. Next, we design the first two pilot blocks within each frame and develop a serial low-complexity channel estimation algorithm. Specifically, the channel estimates are initially obtained, and each estimate is further expressed as the sum of its perfectly known value and the estimation error. By leveraging the relationship between channels for the two pilot blocks, we recover the Doppler shifts from the channel estimates, based on which we can further acquire other channel parameters. Moreover, the Cramer-Rao lower bound (CRLB) for each parameter is derived as a performance benchmark. Finally, we provide numerical results to establish the effectiveness of our proposed estimators.

Channel Estimation for High-Speed Railway Wireless Communications: A Generative Adversarial Network Approach

In high-speed railways, the wireless channel and network topology change rapidly due to the high-speed movement of trains and the constant change of the location of communication equipment. The topology is affected by channel noise, making accurate channel estimation more difficult. Therefore, the way to obtain accurate channel state information (CSI) is the greatest challenge. In this paper, a two-stage channel-estimation method based on generative adversarial networks (cGAN) is proposed for MIMO-OFDM systems in high-mobility scenarios. The complex channel matrix is treated as an image, and the cGAN is trained against it to generate a more realistic channel image. In addition, the noise2noise (N2N) algorithm is used to denoise the pilot signal received by the base station to improve the estimation quality. Simulation experiments have shown the proposed N2N-cGAN algorithm has better robustness. In particular, the N2N-cGAN algorithm can be adapted to the case of fewer pilot sequences.

Two-Stage Traffic Load Prediction-Based Resource Reservation for Sliced HSR Wireless Networks

In this letter, we propose a two-stage traffic load prediction scheme for network slices (NSs) in high-speed railway (HSR) wireless networks, where in the first stage, the K-means algorithm is leveraged to cluster traffic flows, and in the second stage, the long-short term memory (LSTM) algorithm is applied to predict the traffic load. Based on the obtained traffic features (including traffic volume and user velocity) and the network radio resource characteristics (including coverage performance and capacity), we design a service-tailored resource reservation mechanism. Simulation results show that our proposed scheme can significantly improve the traffic load prediction accuracy to ensure the NS resource reservation performance.

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.

Joint Doppler Shift and Channel Estimation for High-Speed Railway Wireless Communications in Tunnel Scenarios

In this work, we study the problem of Doppler shift and channel estimation for wireless communication systems on high-speed railways (HSRs). We focus on tunnel scenario, one of the classical scenarios of HSRs. We first build up the mathematical system model, design a joint Doppler shift and channel estimator, and compare its performance with the typical Moose algorithm. We show that our estimator outperforms the Moose algorithm in Doppler estimation. Besides, since wireless channels in tunnel scenarios often contain several or multiple taps, we suggest an adaptive frame structure to improve transmission efficiency. Simulations are then provided to corroborate our proposed studies.

Wireless Channel Modeling Based on Machine Learning in the High-speed Railway Scenario

In the high-speed railway (HSR) scenario, there is a problem of poor generalization ability caused by the overfitting phenomenon through wireless channel modeling. In this paper, a precise modeling method based on machine learning is proposed to address this issue under the wireless channel of HSR. According to the K-means clustering algorithm, the typical values of the K-factor in a multitude of scenarios are obtained through Rician K-factor clustering in the wireless channel fading model. Founded on the theory of model evaluation and selection in machine learning, the measured values of path loss are fitted by least squares regression using the cross-validation method. Then, we obtain the formulas of the path loss models and the expected generalization errors of the models in different scenarios. The mathematical relationship of the ergodic capacity depending on the Rician K-factor, the signal-noise ratio of the receiver, and the path loss generalization error are established after the ergodic capacity analysis of the HSR wireless channel. Finally, the lower boundary curves of the ergodic capacity are obtained by simulation experiments in different scenarios.

Secrecy Performance Analysis of the NOMA System on High-Speed Railway

High-speed railway (HSR) wireless communications are required to ensure strict security. In this work, we study the secrecy performance of a nonorthogonal multiple access- (NOMA-) aided HSR wireless communication system in the case of an eavesdropping user. Specifically, applying NOMA technology to the HSR communication system can effectively improve the data rates. Therefore, we study the secrecy performance of the downlink NOMA system under the HSR wireless communication. In particular, the exact analytical results for the secrecy outage probability (SOP) based on no small-scale channel state information (CSI) are derived. We also provide all of the parameterizations for the proposed channel model. Finally, the correctness of theoretical derivation is verified via simulations. Results show the positive effect of utilizing the NOMA for enhancing wireless systems secrecy performance.

AI-Enabled Sub-6-GHz and mm-Wave Hybrid Communications: Considerations for Use With Future HSR Wireless Systems

Due to abundant spectrum resources, communications in the millimeter-wave (mm-wave) bands have been widely considered as the key enabler for next-generation wireless systems, including future high-speed railway (HSR) wireless systems, to dramatically increase data rates. To overcome the hostile propagation properties of mm-wave bands, antenna arrays, which enable directional transmissions with extended transmission distance, become essential techniques. Nevertheless, the high cost of antenna arrays and the limited battery lifetime of user equipment are slowing down the practical deployment of mm-wave communications. Without these limitations, an HSR scenario is better suited for mm-wave communications. However, the point-like coverage of mmwave directional beams cannot provide robust access for high-mobility scenarios.

Modeling and Optimization of Wireless Channel in High-Speed Railway Terrain

The high-speed railway (HSR) wireless channel models based on field measurements have poor universality and low modeling accuracy due to the limitations of the experimental methods and the terrain conditions. To overcome this problem, this paper considers the wireless channels in various HSR scenarios (such as tunnels, mountains, viaducts, cuttings and plains) as the research objects and establishes a novel finite-state Markov chain (FSMC) optimization simulation model based on the signal-to-noise ratio (SNR) threshold, the channel states and the state transition probability matrix, by using the nonuniform space division SNR quantization strategy (hereinafter referred to as Strategy 1) and the equal-area space division SNR quantization strategy (hereinafter referred to as Strategy 2). The SNR curves that are obtained via simulation closely fit the experimental results; therefore, the proposed simulation model can accurately characterize the channel state in a variety of HSR scenarios. Furthermore, the simulation results demonstrate that in the tunnel scenario, Strategy 1 realizes a smaller mean square error (MSE) and a higher modeling accuracy than Strategy 2. The MSE values of the two strategies are similar in the plain scenario. Strategy 2 realizes a smaller MSE and a higher modeling accuracy in the mountain, viaduct and cutting scenarios.

An Improved Interference Alignment Algorithm With User Mobility Prediction for High-Speed Railway Wireless Communication Networks

The enhancement of the carrying capacity of high-speed railway and the acceleration of train speed lead to the proliferation of signal interference in the communication network, which leads to the degradation of network performance and user service quality. To address the issue, we first classify the cell users in high-speed railway wireless communication environment based on Fuzzy C-Means algorithm and user mobility prediction model, and divide communicating users on trains into cell center users and edge users. Then, differential power distribution schemes are implemented for center users and edge users according to the classification results. Finally, the Max-SINR based interference alignment algorithm is used to realize the interference management. Simulation results show that the proposed algorithm fully takes into account the mobility of high-speed train users and effectively manages interference, which improves the high-speed railway wireless communication network performance significantly.

Stable Beamforming With Low Overhead for C/U-Plane Decoupled HSR Wireless Networks

Millimeter wave (mmWave) directional communications have become a vital 5G technique to achieve the goal of multiple gigabit data rate with low latency, which also hold true for high-speed railway (HSR) wireless networks. However, the high mobility in HSR scenarios causes frequent beam realignments and consequently high feedback overheads, degrading the transmission reliability, and efficiency. In mmWave beamforming systems, besides transmitters (TXs) and receivers (RXs) are also equipped with antenna arrays, which equivalently introduces a new degree of freedom for link adaptation. Based on this observation, a stable beamforming scheme with low overhead, consisting of both long-term TX beamforming and short-term RX beamforming, is proposed. In the long-term TX beamforming, under the premise of satisfying the basic receiver sensitivity, TXs are devised to use as wide beams as possible to extend the serving time of each beam. In the short-term RX beamforming, within a TX beam, RXs autonomously adjust RX beams according to the current channel state information instead of frequently feeding it back to TXs, thereby reducing feedback overheads and accelerating link adaptation. Through this scheme, the received signal to noise ratio is kept at a high and stable level. To meet the strict transmission reliability requirements in HSRs, the control/user-plane decoupled architecture is applied to mitigate the inherent blindness of mmWave directional beams. Theoretical and numerical results show that the proposed scheme can significantly reduce feedback overheads and guarantee the desired performance.

Location-Aware ICI Reduction in MIMO-OFDM Downlinks for High-Speed Railway Communication Systems

High mobility may destroy the orthogonality of subcarriers in OFDM systems, resulting in inter- carrier interference (ICI), which may greatly reduce the service quantity of high-speed railway (HSR) wireless communications. This paper focuses on ICI mitigation in the HSR downlinks with distributed transmit antennas.For such a system, its key feature is that the ICIs are caused by multiple carrier frequency offsets corresponding to multiple transmit antennas. Meanwhile, the channel of HSR is fast time varying, which is another big challenge in the system design. In order to get a good performance, low complexity real-time ICI reduction is necessary. To this end, we first analyzed the property of the ICI matrix in AWGN and Rician scenarios, respectively. Then, we propose corresponding low complexity ICI reduction methods based on location information. For evaluating the effectiveness of the proposed method, the expectation and variance of remaining interference after ICI reduction is analyzed with respect to Rician K-factor. In addition, the service quantity and the bandwidth and computation cost are also discussed. Numerical results are presented to verify our theoretical analysis and the effectiveness of proposed ICI reduction methods. One important observation is that our proposed ICI mitigation method can achieve almost the same service quantity with that obtained on the case without ICI at 300km/h,that is, ICI has been completely eliminated. Numerical results also show that the scenarios with Rician K-factors over 30dB can be considered as AWGN scenarios, which may provide valuable insights on future system designs.

Optimal Nonuniform Steady mmWave Beamforming for High-Speed Railway

Using higher frequency bands (e.g., millimeter waves) to provide higher data rate is an effective way to eliminate performance bottleneck for future wireless networks, particularly for cellular networks based high-speed railway (HSR) wireless communication systems. However, higher frequency bands suffer from significant path loss and narrow-beam coverage, which pose serious challenges in cellular networks, especially under the HSR scenario. Meanwhile, as one of the key performance indexes of ultrareliable and low-latency communications in 5G systems, network reliability should be guaranteed to provide steady reliable data transmission along the railway, especially when safety-critical railway signaling information is delivered. In this paper, we propose a novel beamforming scheme, namely, optimal nonuniform steady mmWave beamforming, to guarantee the network reliability under an interleaved redundant coverage architecture for future HSR wireless systems. Moreover, we develop a bisection-based beam boundary determination (BBBD) method to determine the service area of each predefined RF beam. Finally, we demonstrate that the proposed optimal nonuniform steady mmWave beamforming can provide steady reliable data transmissions along the railway, and the network reliability requirements can be guaranteed when the proposed BBBD method is used. We expect that our optimal nonuniform steady mmWave beamforming provides a promising solution for future HSR wireless systems.

Service-Oriented Power Allocation for High-Speed Railway Wireless Communications

The rapid development of high-speed railways (HSRs) all over the world is drawing much more attention on high-mobility wireless communication. For the wireless links that connect the passengers on the train to the cellular network, it is very essential to employ an appropriate power allocation strategy to guarantee the reliability and efficiency of information transmission. Therefore, this paper concentrates on evaluating the transmission performance of wireless links in the HSRs and attempting to derive an optimal power allocation strategy of this scenario. Considering the fact that the information transmitted between the train and the base station usually has diverse quality-of-service (QoS) requirements for various services, a QoS-based achievable rate region is utilized to characterize the transmission performance in this paper to instead of traditional single throughput. Based on it, a QoS-distinguished power allocation algorithm is derived to achieve the largest achievable rate region. It is proved that the traditional water-filling algorithm and the channel inverse algorithm can be regarded as two specific cases of this new algorithm. Besides, the robust performance of the proposed strategy is also discussed in detail under a non-uniform motion scenario, and its relative performance loss is evaluated in terms of energy consumption. Finally, we present a typical implementation example in a Rice fading environment when the data rate requirements are prior known.

Distributed and Dynamic Resource Management for Wireless Service Delivery to High-Speed Trains

With the fast development of high-speed railway (HSR), how to provide high-quality and cost-effective wireless services for HSR users has attracted increasing attention in recent years. A key issue is to design an efficient resource management scheme for wireless service delivery between the train and the ground. In this paper, we first provide an overview of the existing resource management schemes and some unsolved challenges are identified. To address these challenges, a cross-layer optimization framework is developed for facilitating the design and optimization of dynamic resource management. Then, the resource management problem is formulated as a stochastic optimization problem, which jointly considers the quality-of-service requirements and dynamic characteristics of HSR wireless communications. The stochastic network optimization theory is applied to transform the intractable stochastic optimization problem into a tractable deterministic optimization problem, which can be further decomposed into two separate subproblems: admission control and resource allocation. A fully distributed admission control scheme is proposed for the admission control subproblem, and a cooperative distributed resource allocation scheme is developed for the mixed-integer resource allocation subproblem with guaranteed global optimality. Finally, a distributed dynamic resource management algorithm is proposed to solve the original stochastic optimization problem with high reliability and robustness against central node failure. The performance of the proposed algorithm is analyzed theoretically and further validated by numerical simulations under realistic conditions for HSR wireless communications.

Dynamic resource allocation for multimedia services delivery in cellular/infostation integrated HSR networks

With the rapid development of high-speed railway (HSR) system, the resource allocation problem in HSR wireless network becomes one of the key issues to improve the efficiency of resource utilisation. In this paper, we investigate the downlink resource allocation problem for multimedia services delivery in HSR multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) system with a cellular/infostation integrated network architecture. Taking the train trajectory and network stability into account, we formulate the problem as a stochastic network optimisation programming, which aims at maximising the overall system utility while keeping the system stable under the total transmission power constraint. To address the NP-hard mixed integer programming, the original problem is firstly transformed into a queue stability problem, and then decomposed into two separate subproblems by the drift-plus-penalty (DPP) approach. Finally, based on the stochastic optimisation technique, a dynamic resource allocation algorithm is proposed and its efficiency is illustrated by theoretical analysis and numerical simulations.

Modeling link quality for high-speed railway wireless networks based on hidden Markov chain

In high-speed railway (HSR) wireless networks, the link quality is greatly time-dependent and location-varying. Due to the high randomness, it is challenging to predict the link quality in HSR wireless networks. In this paper, we firstly conducted a certain amount of field measurement campaigns of HSR wireless network link quality. A great number of practical datasets are collected regarding packet loss rate (PLR) and round-trip time (RTT). Then, we analyzed its changing pattern in different time scales, and further model the link quality of HSR wireless network using hidden Markov chain. Based on this, an improved algorithm was developed to simulate the variation of HSR wireless network link quality. Simulation results prove that the proposed model is capable of accurately reproducing the behavior of HSR wireless network link quality with regard to PLR and RTT. This work will offer new inspiration to the prediction of link quality for HSR wireless networks.

Power-space functions in high speed railway wireless communications

To facilitate the base station planning in high speed railway communication systems, it is necessary to consider the functional relationships between the base station transmit power and space parameters such as train velocity and cell radius. Since these functions are able to present some inherent system properties determined by its spatial topology, they will be referred to as the power-space functions in this paper. In light of the fact that the line-of-sight path persists the most power of the received signal of each passing train, this paper considers the average transmission rate and bounds on power-space functions based on the additive white Gaussian noise channel (AWGN) model. As shown by Monte Carlo simulations, using AWGN channel instead of Rician channel introduces very small approximation errors, but a tractable mathematical framework and insightful results. Particularly, lower bounds and upper bounds on the average transmission rate, as well as transmit power as functions of train velocity and cell radius are presented in this paper. It is also proved that to maintain a fixed amount of service or a fixed average transmission rate, the transmit power of a base station needs to be increased exponentially, if the train velocity or cell radius is increased, respectively.