To enhance the reliability of integrated sensing and communication (ISAC) in high-speed railway (HSR) systems, this paper proposes a reconfigurable intelligent surface (RIS)-assisted beamforming framework with active sensing capabilities. First, a Doppler frequency shift compensation (DFSC) method is introduced based on sensing echo signals. A long short-term memory (LSTM) network is employed to predict real-time train speed during satellite coverage gaps, while a multiple signal classification (MUSIC) algorithm with a fine search strategy estimates the channel direction of arrival. These estimates enable optimized RIS phase shifts for Doppler pre-compensation. Furthermore, a twin delayed deep deterministic policy gradient (TD3) algorithm integrated with prioritized experience replay (PER) is developed to jointly optimize the achievable sum rate (ASR) and sensing performance. Simulation results demonstrate that at a train speed of 70 m/s, the proposed method achieves a 7.50% improvement in ASR compared to the system without compensation. Moreover, the PER-TD3 algorithm effectively maximizes the ASR while maintaining the required positioning accuracy, validating its suitability for high-mobility ISAC scenarios.

In multi-scenario Mobile Edge Computing (MEC), offloading and resource allocation for topologically heterogeneous Directed Acyclic Graph (DAG) tasks are NP-hard, while Federated Meta-Learning (FML) suffers from generalization degradation under non-independent and identically distributed data. To address both challenges, this paper proposes a Difficulty-Aware FML (DAFML) algorithm. We construct a three-dimensional difficulty metric—integrating task load, topological complexity, and resource supply–demand tension—and determine its weights via the Analytic Hierarchy Process. Building on this metric, a difficulty-guided non-uniform meta-task sampling strategy prioritizes complex tasks, while Proximal Policy Optimization (PPO) handles inner-loop adaptation. The offloading problem is formulated as minimizing a normalized composite cost of latency, energy, and deadline violations. Simulation results show that DAFML achieves a composite cost of 0.8096 and a completion rate of 82.61%, outperforming Greedy, centralized PPO, FedAvg-PPO, and Fed-MAML. Compared with Fed-MAML, DAFML reduces the composite cost by 2.43% and improves the completion rate by 2.26 percentage points, with greater gains on complex subsets. Ablation studies validate the contributions of the sampling strategy and the three difficulty dimensions, and convergence analysis reveals that DAFML has the most stable trajectory among all algorithms.

As the railway industry undergoes digital transformation driven by AI, big data, and 5G/6G, secure data transmission for IoT-oriented railway operations has become a critical challenge in high-speed railway (HSR) wireless communications. Covert communication, which conceals wireless transmissions from eavesdroppers, together with reconfigurable intelligent surfaces (RISs), MIMO, and millimeter-wave technologies, offers a promising solution for enhancing security in such dynamic environments. This paper presents a comprehensive review of RIS-assisted covert communication for mmWave MIMO channels in HSR IoT networks. We examine security threats and the limitations of existing schemes, introduce the system architecture and channel modeling approaches, and summarize Doppler shift compensation techniques for high-mobility scenarios. In particular, this review introduces a service-priority-based covert constraint design framework as an original analytical perspective, rather than a fully validated engineering solution, to improve adaptability in diverse HSR operational contexts. We further provide a critical comparative review of optimization algorithms, including convex relaxation, heuristic, and deep reinforcement learning approaches, with a focus on their applicability and complexity trade-offs, and outline future research directions. Beyond summarizing the existing literature, this review integrates new analytical perspectives and simulation-based illustrations derived from our recent publications, offering both a systematic reference and forward-looking insights for researchers in this emerging field.