Yifei Cai

The intelligent development of high-speed railways (HSRs) necessitates support for various services to ensure safe and reliable train operations while providing high-quality travel experiences for passengers. Network slicing presents a promising solution via isolated and service-specific radio resource management. However, meeting heterogeneous quality of service (QoS) requirements in HSR communications is particularly challenging due to imperfect channel state information (CSI) caused by high-speed mobility. In this work, we investigate a slicing puncture strategy in a moving network with an imperfect train-to-ground downlink, supporting passenger entertainment and safety-related services. The system includes two transmission links: outboard and inboard. Given the impact of high-speed mobility, we characterize the statistical probability distribution of the actual CSI and model the average transmission rates in the outboard link. We aim to minimize the system Resource Block (RB) and power in the above two links while satisfying the diverse QoS requirements of services. Since the resource minimization problem is a mixed-integer nonlinear programming, we decompose it into three subproblems: RB allocation, power allocation, and slicing puncture optimization. A Speed-Aware Resource allocation and Slicing puncture (SA-RS) algorithm is proposed. Specifically, analytical expressions are derived for RB allocation, and a bisection-based algorithm is designed for power allocation. Moreover, the slicing puncture strategy is obtained using a genetic-based algorithm. Simulation results demonstrate that the proposed strategy can improve the system performance compared with other baseline schemes under imperfect CSI.

To address the issues of low accuracy and poor adaptability in traditional dynamic parameter identification methods for heavy-haul trains, this paper proposes a Polynomial Nonlinear Recursive Least Squares (PNRLS) algorithm. Conventional approaches, such as static quadratic empirical models, fail to effectively decouple multi-source disturbances (e.g., wheel-rail wear and aerodynamic effects), while least squares (LS)-based methods lack robustness in estimating time-varying parameters under noisy or incomplete data conditions. The PNRLS algorithm constructs a multidimensional linear dynamic model by expanding the dimensionality of the system equation through the Kronecker product, thereby integrating both low-order and high-order variables. Simultaneously, by iteratively optimizing weight factors and recursively applying weighted least squares (WLS) in high-dimensional space, it significantly enhances the accuracy and stability of parameter estimation, reduces reliance on historical data, and improves the utilization efficiency of high-order system information, effectively mitigating matrix illconditioning and reducing identification lag for time-varying parameters. Experimental validation using real traction force and velocity data from the HXD1 heavy-haul train demonstrates an average improvement of 13.62% in the estimation accuracy of basic resistance and rotational mass coefficients compared to LS, FF-RLS, and RMLE methods, thereby significantly enhancing the accuracy and stability of parameter identification and confirming its superior performance under complex multi-source disturbances and data noise conditions.

Wireless train communication network (WLTCN) is an emerging technology for enabling intelligent rail vehicles. It is responsible for providing train control services (TCS), passenger information services (PIS), and train sensing services (TSS). These services within WLTCN have notably different quality of service (QoS) requirements from traditional telecommunication services. In this paper, to incorporate multiple services in a single WLTCN, we propose a radio access network (RAN) slicing architecture empowered WLTCN to satisfy the demands of services and save bandwidth resource. In particular, the service and slicing models of TCS, PIS, and TSS are investigated. By analyzing the heterogeneous characteristics and QoS requirements of the above services within WLTCN, we exploit the orthogonal multiple access scheme for TCS and PIS and the non-orthogonal multiple access scheme for TSS, respectively. The system bandwidth minimization problem is formulated with slicing resource allocation for TCS, PIS, and TSS and non-orthogonal access grouping for TSS terminals as a mixed-integer nonlinear programming (MINLP). To solve the intractable MINLP, the original problem is transformed and decoupled into the two subproblems. Then, we propose a joint bandwidth optimization and terminal clustering (JBOTC) algorithm to tackle the bandwidth allocation problem with optimal terminal grouping strategy for TSS effectively. The closed-form expressions of the optimal bandwidth allocation strategy for three services are derived. The simulation results illustrate the performance superiority for saving bandwidth of the JBOTC algorithm to the benchmark schemes. Our proposed slicing strategy enables WLTCN to support heterogeneous services co-existence with minimal bandwidth consumption.