With the booming development of high-speed railways (HSRs), key technologies of wireless communications need to be constantly innovated. In particular, the frontier issue of low delay of the handover for the fifth generation (5G) in fast-moving scenarios has attracted attention from both industry and academia. Based on an analysis of a large number of measured data and the location of the user equipment (UE), a fast handover algorithm is proposed to solve the problem of long delay for a train moving at high speed in a 5G-railway (5G-R). By calculating the speed of a train and its direction of movement, a reasonable handover mode is selected and the handover chain of neighboring cells is identified. The location of the train can be calculated to determine whether UE enters the defined identification zone of pre-handover. Depending on the values collected in the measurement report, the command of the handover is triggered when the weight of the target cell is greater than that of the source cell. Our experimental results show that the delay of the fast handover algorithm is reduced by 2.03%, and the success rate of the handover is increased by 0.42%. Research directions for smart railways are discussed based on these findings.

How to improve energy efficiency (EE) and transmission efficiency for wireless sensor networks (WSNs) through dynamic routing is a challenge. This article introduces MADII, an adaptive routing algorithm integrating the informer model and imitation learning (IL) within a multiagent deep Q-network (DQN) framework. Our method models the routing decision in WSN as a multiagent Markov decision process (MMDP) and employs a novel sensor transmission control mechanism for information via negotiation in centralized-training, centralized-execution and centralized-distribution (STCIN3C) tailored for the centralized training and centralized execution (CTCE) paradigm. This design effectively minimizes redundant energy consumption of sensor nodes (SNs). The reward function of reinforcement learning (RL) is designed by considering transmission quantity, data-receiving success rate, and relative energy consumption. Furthermore, our approach adopts a combination of informer model and IL strategy for balancing exploration and exploitation of RL applications with large-scale action space through deep learning (DL) model and training sample, which can accelerate convergence speed, fit the dynamically changing features of WSN, and obtain better routing scheme. Extensive experiments demonstrate that MADII significantly outperforms existing methods on network lifetime and EE. Furthermore, the ablation experiment emphasizes the effectiveness of informer model and IL strategy in dealing with RL with large-scale action space.

This paper proposes an intelligent reflecting surface (IRS)-assisted energy efficiency optimization algorithm to address the problem of energy efficiency (EE) degradation in high-speed rail communication systems caused by line-of-sight link blockages between base stations and trains. The joint optimization of base station beamforming and IRS phase shifts is formulated as a variable-coupled energy efficiency maximization problem, subject to the base station’s transmission power and the IRS unit’s modulus constraints. This is known to be an NP-hard problem, making it challenging to obtain the global optimal solution. To tackle the issue of optimization variable coupling, an alternating optimization is employed to decompose the original problem into two sub-problems: base station beamforming and IRS phase-shift optimization. The Dinkelbach method is utilized to convert the fractional objective function into a difference form; then, the successive convex approximation (SCA) algorithm is applied to transform non-convex constraints into convex ones, which are solved using CVX. The Riemann conjugate gradient (RCG) algorithm can effectively solve the difficult unit module constraint. Finally, an alternating iterative strategy is employed to converge to a suboptimal solution. Our simulation results demonstrate that the proposed algorithm significantly enhances system efficiency with low computational complexity. Specifically, when the number of IRS reflecting elements is 64, the system’s EE is improved by approximately 12.41%, 35.26%, and 37.96% compared to the semi-definite relaxation algorithm, the random phase shift approach, and no IRS scheme, respectively.

The paper studies the cooperative spectrum sharing among multiple secondary users (SUs) in a clustering cognitive ad hoc network. The problem is formulated as a repeated game with the aim of maximizing the total transmission rate of SUs. Firstly, a clustering formation procedure is proposed to reduce the overhead and delay of game process in cognitive radio network (CRN). Then the repeated game-inspired model for SUs is introduced. With the model, the convergence condition of the proposed spectrum-sharing algorithm is conducted, and the convergence performance is investigated by considering the effects of three key factors: transmission power, discount factor, and convergence coefficient. Furthermore, the fairness of spectrum sharing is analyzed, and numerical results show a significant performance improvement of the proposed strategy when compared to other similar spectrum-sharing algorithms.

This paper proposed a clustering routing protocol for Wireless Sensor Network (WSN) under solar energy harvesting to tackle the issue of insufficient energy storage. During the clustering stage, the protocol utilizes genetic algorithm to enhance the issue that the K-Means calculation is sensitive to outliers, and designs an efficient fitness function to iteratively find the global optimal initial clustering center; This protocol dynamically update the cluster head by incorporating the node residual energy, node harvested energy and node position into the cluster head update criterion; In the data transfer stage the node sleep scheduling mechanism is introduced during the data transmission stage to balance the network energy consumption. Through simulation and correlation of LEACH, LEACH-improved and SEP protocols, the protocol actually stretch life cycle and further develop throughput of the network.

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.