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.

Radio spectrum is one of the most scarce and valuable resources for wireless communications. Cognitive radio has been considered as an efficient means to opportunistic spectrum sharing between primary (licensed) users and cognitive radio users. In this paper, based on the two-phase channel and power allocation scheme proposed by A. T. Hoang etc., we present an opportunistic spectrum access approach for cognitive radio network. In the scheme proposed by A. T. Hoang etc., they consider a cognitive radio network that consists of multiple cells and the system throughput is defined as the total number of subscribers that can be simultaneously served. In this paper, we consider a cognitive radio network as self-organizing network. Furthermore, the throughput is defined as the average probability of success transmission. In our proposed approach, for each available channel, TDMA frame consists of N time slots, and each active cognitive user is assigned one transmission slot different from those of other active cognitive users in each frame. It allows an active cognitive user utilize the slots pre-assigned to the other active cognitive users under a range of values for accessing opportunity. We evaluate the performances of the opportunistic spectrum access approach in view of system throughput and energy efficiency.