High-speed railway (HSR) communication is subject to unauthorized eavesdropping, and the acquisition of perfect channel state information (CSI) is difficult, which increases the secrecy outage probability of the system and poses a threat to secure data transmission. In addition, the train’s operating environment is complex. Deviations in train speed can increase Doppler shift compensation errors, further increases the secrecy outage probability. This paper constructs a train speed prediction model based on the L-Ns-Transformer. By compensating for Doppler shift, the model achieves more accurate channel modeling. Furthermore, an intelligent reflecting surface (IRS)-assisted beamforming method for HSR secure communication is proposed when the eavesdropper’s (Eve) CSI is unknown. We propose a two-stage deep learning (TS-DL)-based approach to design transmitter beamforming and IRS jointly, where the precoding vector and phase shift matrix are designed to minimize the secrecy outage probability. Simulation results demonstrate that the proposed TS-DL approach has lower computational complexity and can effectively reduce the system secrecy outage probability, thereby enhancing the security of HSR wireless communication.

Abstract The AIoT, with its artificial intelligence capabilities, can further enhance Device‐to‐Device (D2D) communication. Based on Non‐Orthogonal Multiple Access (NOMA), D2D technology can effectively alleviate wireless spectrum resource pressure and improve the capacity of heterogeneous cellular networks. However, it also introduces significant system interference issues. In this paper, a resource allocation algorithm is proposed for the NOMA‐D2D heterogeneous cellular network, based on a multi‐agent deep reinforcement learning framework. Firstly, the algorithm allocates appropriate channels to D2D clusters. Then, the power allocation factors and D2D transmit power are jointly optimized to suppress the interference and improve the system performance. Simulation results show that both the channel allocation efficiency and the power control performance of the system can be significantly improved.

Recently, in order to extend the computation capability of smart mobile devices (SMDs) and reduce the task execution delay, mobile edge computing (MEC) has attracted considerable attention. In this paper, a stochastic optimization problem is formulated to maximize the system utility and ensure the queue stability, which subjects to the power, subcarrier, SMDs, and MEC server computation resource constraints by jointly optimizing congestion control and resource allocation. With the help of the Lyapunov optimization method, the primal problem is transformed into five subproblems including the system utility maximization subproblem, SMD congestion control subproblem, SMD computation resource allocation subproblem, joint power and subcarrier allocation subproblem, and MEC server scheduling subproblem. Since the first three subproblems are all single variable problems, the solutions can be obtained directly. The joint power and subcarrier allocation subproblem can be efficiently solved by utilizing alternating and time‐sharing methods. For the MEC server scheduling subproblem, an efficient algorithm is proposed to solve it. By solving the five subproblems at each slot, we propose a delay‐aware task congestion control and resource allocation (DTCCRA) algorithm to solve the primal problem. Theoretical analysis shows that the proposed DTCCRA algorithm can achieve the system utility and execution delay trade‐off. Compared with the intelligent heuristic (IH) algorithm, when the control parameter V increases from 106 to 107, the total backlogs are decreased by 5.03% and the system utility is increased by 3.9% on average for the extensive performance by using the proposed DTCCRA algorithm.

With the continuous evolution of wireless communications, the higher operating frequency of 5th generation (5G) makes the coverage ability of radio frequency (RF) units become more inadequate, especially in the high-speed railway scenario (HSRS). For the design of coverage, methods are proposed in the areas of the tunnel, non-tunnel and entrance to tunnel respectively. The spacing of new radio node base stations (gNBs) can be calculated by the analysis of the incident angle, the measurement of penetration loss and the control of cell handover. Under the same parameter setting, the coverage distance of high-gain antenna with lens array is greatly improved compared with traditional antenna. The technology of multi-cell merging reduces the number of cell handover several times. The suppression of electromagnetic interference can be realized by reasonably calculating and correcting the doppler frequency shift. Results show that the length and spacing of leakage cable slots and the working frequency used are strongly correlated with the performance. The antenna with lens array has the obvious advantages in RF gain. After application in HSRS, the rate of downloading reaches more than 200Mbps and the rate of uploading is greater than 20Mbps.

Accurate localization of railway vehicles is one of the key requirements for development of high-efficiency automatic train control systems. This paper proposes an angle offset-assisted positioning (AOAP) scheme for improving the localization of railway vehicles in tunnel environments. Our AOAP system consists of a wireless sensor network (WSN), where anchor nodes or sensors are distributed along the railway tracks to collect the signals transmitted by a target node installed a train. Based on the information obtained by the anchor nodes from the target node, the position of the target node or the train is initially estimated. However, radio signals experience transmission delay, which makes this estimated position have a bias from the real position. Hence, the initially estimated train's position is updated with the aid of the angle offset estimated. In this contribution, we address the effect of angle offset error on the positioning accuracy. It can be shown that our AOAP approach is capable of providing more accurate positioning than the conventional least square approach without considering angle offset.