With the rapid development of vehicular edge computing (VEC) and artificial intelligence (AI), the emergence of vehicle edge intelligence meets the need for real-time vehicle intelligence applications. But the execution of deep neural networks (DNNs) requires a large amount of data input, which results in a large amount of computing resources required for the execution of DNN tasks. This also brings a certain burden to the deployment of DNN tasks and the resource allocation of edge servers. In addition, due to the high mobility of vehicles in the VEC, the backhaul delay of vehicle edge intelligent task results increases, affecting the vehicle’s quality of experience (QoE). We propose a joint optimization strategy for service migration and resource allocation aimed at minimizing the average task completion delay. This strategy comprehensively considers service migration actions and edge server resource allocation, which is proved to be a mixed integer nonlinear programming (MINLP) problem, and hence we formulate it as an Markov decision process (MDP). To solve this problem, we propose a service migration algorithm based on the self-attention mechanism-based double deep Q-network and deep deterministic policy gradient algorithm (SA-DDQN-DDPG) to solve it to obtain the optimal system service migration strategy. The experimental results show that the proposed SA-DDQN-DDPG algorithm has good performance in reducing latency. The average migration latency is reduced by 40.41%, 20.7%, and 14.50% compared with always, DQN and DDQN, respectively.

With the rapid development of intelligent transportation systems, multimodal applications such as autonomous driving and real-time video analytics are increasingly common. Cloud-edge-end computing has emerged as a promising solution to support these latency-sensitive tasks through distributed computing and edge content delivery. However, in urban hotspot areas, limited resources and frequent backhaul transmissions degrade network performance. Optimizing content caching to reduce task execution remains a key challenge. To address this, Unmanned Aerial Vehicles (UAVs) are introduced into vehicular networks due to their low cost and high mobility, serving as aerial base stations to assist ground infrastructure. We propose an cloud-edge-end collaborative caching framework, deploying algorithms on UAVs with computing and storage capabilities, working with Roadside Units (RSUs) and idle vehicles to alleviate resource constraints in hotspots. Within this framework, we apply the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm for UAV deployment optimization. Then, we propose a content request prediction model using Bidirectional Gated Recurrent Unit (Bi-GRU) and attention mechanisms. Finally, a content caching algorithm based on Soft Asynchronous Advantage Actor-Critic with Action Mask Module (SA3C-AM) is introduced to minimize latency. Experimental results show that compared to baseline methods, our approach improves cache hit rate by 15.4%, reduces content fetches by 34.08%, and lowers average request latency by 17.6%.

With the intensive deployment of 5G networks in low-frequency bands, the potential impact of their Adjacent-frequency interference on radio astronomy systems has attracted wide attention. In this paper, based on the propagation model and protection standard recommended by the International Telecommunication Union (ITU-R), the coexistence conditions with Adjacent-frequency radio astronomy systems are investigated for the spurious transmitter interference of 5G terminals in the 1800 MHz band. The geospatial separation distance requirements under different observation modes are calculated by combining the ITU-R P.452 propagation model and the P.2108 clutter loss model through the deterministic analysis method. The results show that under the "worst case interference scenario", the 5G system needs to maintain an isolation distance of at least 1.49 km (for spectral line observation) or 4.9 km (for continuous observation) from the radio astronomy station. The research results can provide technical basis for 5G spectrum planning and radio astronomy station protection.

As a distributed embedded system, vehicular edge computing (VEC) completes various complex Deep neural network (DNN) tasks through network collaboration and communication. However,due to the limited computing power of vehicle processors, vehicles cannot handle increasingly complex DNN tasks. To accurately estimate the execution latency of each layer across different DNN models on heterogeneous devices, we proposed the Extreme Gradient Boosting Tree (XGBoost) algorithm to predict DNN task inference latency. Furthermore, we proposed partitioning and offloading algorithms for both chained DNN tasks and Directed Acyclic Graph (DAG)-type DNN tasks, addressing their unique computational characteristics. For chained DNN tasks, we employ a linear search to determine optimal partitioning points based on predictions from the DNN latency prediction model. For the partitioning and offloading of DAG-type DNN tasks, we construct it as a minimum cut problem under the network flow graph and propose a DNN task partitioning and offloading algorithm based on the highest label pre-stream push (HLPP) algorithm to effectively reduce the cost of task partitioning and offloading. Finally, we used an experimental vehicle equipped with Raspberry and a RSU equipped with Jetson Nano to verify the results. The experiment shows that the DNN latency prediction model based on the XGBoost we proposed can effectively improve the latency prediction accuracy of DNN layer-by-layer execution. At the same time, the division and offloading algorithms for different types of DNN inference tasks can achieve higher task completion rate, lower latency, and lower energy consumption.

With the rapid development of various industries, the demand for instant communication has surged, and interphones are widely used in various fields such as civil aviation, fire protection, public security, and property management. Due to the mature technology, competitive pricing, and large market stock of analog products, traditional analog FM interphones still dominate the market. In the situation where frequency resources are becoming increasingly scarce and electromagnetic environments are becoming increasingly complex, interference phenomena of FM radio signals occur frequently. The automatic identification of FM signals is particularly important for maintaining radio order. This paper focuses on the weak links in current FM signal monitoring work and develops a comprehensive, fast, high-precision, and simple FM signal automatic recognition system. It can recognize FM sub tone signals, FM intercom signals and FM broadcast signals in real time, and greatly improve monitoring capabilities and effectively maintain radio order.

Vehicular Edge Computing (VEC) seeks adept task offloading strategies to streamline resource allocation and elevate vehicular application performance. This paper delves into scrutinizing the effectiveness of an Actor-Critic-based Multi-Agent Deep Reinforcement Learning (MADRL) framework in orchestrating cost-minimized task offloading within VEC domains. Embracing the MADRL paradigm, this framework empowers adaptive decision-making for task allocation and execution in highly dynamic vehicular environments. By integrating deep reinforcement learning methodologies into multi-agent systems, the study endeavors to bolster the efficiency of task offloading strategies operating within VEC realms. The research emphasizes MADRL’s role in fostering agile and responsive task allocation paradigms within VEC, aiming to optimize resource utilization while curtailing costs associated with task execution. Through adaptive decision mechanisms, MADRL facilitates dynamic task allocation, enabling vehicles to seamlessly distribute computational tasks among onboard systems and proximal edge resources. This exploration illuminates the potential of MADRL-based strategies in refining task offloading efficiency, presenting a promising avenue for enhancing VEC’s computational resource management and overall performance.

The rapid progression in autonomous driving and vehicle networking technologies has catalyzed the emergence of advanced vehicle applications, aiming to augment traffic safety and the driving experience. This advancement, however, is challenged by the limited computational and storage capacities inherent in on-board vehicle systems. To address this, Vehicle Edge Computing (VEC) emerges as a pivotal solution, enhancing vehicular computational capabilities. In this context, we introduce a novel VEC task offloading model utilizing Deep Reinforcement Learning (DRL). This model leverages the otherwise idle computational resources available in vehicles to facilitate efficient edge computing offloading within heterogeneous networks. A key innovation in our approach is the integration of Reinforcement Learning (RL) with Deep Learning (DL), significantly improving the convergence efficiency of the system. We also introduce an enhanced Q-learning algorithm tailored to jointly address the task offloading and processing challenges in VEC. This algorithm is adept at making optimal offloading decisions, aiming to minimize the overall system cost, encompassing both latency and energy consumption. Through rigorous simulation, our results demonstrate that this improved Q-learning approach substantially reduces total system costs while concurrently improving the quality of service in VEC environments. Our study not only offers a robust framework for computation offloading in vehicular networks but also paves the way for future research in AI-driven vehicular technology optimizations.

The rapid evolution of wireless communication technologies has led to the emergence of 6G networks, promising unprecedented connectivity and data processing capabilities. Within the 6G landscape, vehicle edge computing has gained increasing importance due to its potential to enhance real-time applications and services for connected and autonomous vehicles. However, the seamless integration of vehicles into edge computing environments presents a significant challenge—balancing the trade-off between energy consumption and latency. This research paper explores the critical problem of achieving an equilibrium between energy efficiency and low-latency communication in vehicle edge computing within the context of 6G networks. We propose a novel approach that integrates energy-aware computing strategies and latency optimization techniques to strike the optimal balance between these two conflicting objectives. In the methodology section, we provide a comprehensive overview of our proposed approach, detailing how it measures and evaluates both energy consumption and latency in the context of vehicle-edge interactions. We leverage advanced algorithms and models to efficiently allocate computing resources, optimize task scheduling, and minimize data transmission delays, all while considering the energy constraints of the vehicular devices. To validate the effectiveness of our approach, extensive experiments were conducted in a simulated vehicular edge computing environment. The experimental results demonstrate a substantial reduction in energy consumption while maintaining low-latency communication, outperforming existing methods. We present these results in graphical form, offering clear visual evidence of the improvements achieved.

This comprehensive article delves deeply into the revolutionary implementation of 5G technology in high-speed railway systems, marking a significant leap forward in the realm of connectivity and mobility. It highlights not only the remarkable technological advancements achieved but also the formidable challenges surmounted during this transformative journey. Central to this exploration is the profound impact that 5G technology has had on enhancing both the passenger experience and the overall efficiency of railway operations. The integration of 5G technology in a high-mobility environment is at the forefront of this discussion, inviting a thorough examination of the intricate technical aspects involved. As passengers and railway personnel increasingly reap the benefits of this cutting-edge technology, this article delves into the tangible outcomes and practical implications that have arisen. From lightning-fast internet connectivity during transit to the optimization of train scheduling and maintenance procedures, the ripple effects of 5G in the railway sector are far-reaching. Furthermore, the article sheds light on the potential future developments and opportunities that lie ahead in the realm of 5G technology integration within high-speed railways. As this dynamic field continues to evolve, it becomes evident that the marriage of 5G and high-speed railways is poised to redefine the standards of connectivity and mobility, setting the stage for a truly transformative era in transportation.

Widespread adoption of 5G systems may interfere with fixed satellite service (FSS) earth stations operating in nearby frequency bands. Some countries and regions are currently considering 5G services in the 3.5 GHz range. This study first examines the feasibility of coexistence of satellite FSS operating in nearby frequencies and 5G systems operating in the 3.5 GHz range. The coexistence analysis is performed for the reception of the downlink of a 5G base station in the 3.5 GHz band to a nearby FSS ground station with parameters taken from the ITU-R guidelines related to IMT and FSS systems. The possibility of interference between 5G systems and satellite fixed services, the criteria required for compatibility studies, and the interference deterministic analysis study techniques and results of the study are then presented. Based on a thorough study of interference mitigation strategies that can be used to implement inter-system coexistence, the results of the study outline the protection distances required for the coexistence of 5G systems and FSS ground stations at different spacing frequencies. Finally, the separation distances required for the coexistence of the two systems in adjacent frequencies and the options of interference mitigation techniques that can be considered are given.

In recent years, with the advent of the 5G era, the influence of UAVs Unmanned aerial vehicle is expanding and the civilian UAV market is showing a blowout. Because UAVs are lightweight, flexible and can be used in extreme environments, there are nearly 100 applications in the civilian sector. For areas with poor infrastructure, UAVs can carry base stations to undertake communication tasks. At the same time, collisions between UAVs and interference from ground users have emerged. In this paper, the parameters of international mobile telecommunications (IMT) system UAV-mounted base stations and 1800MHz broadband time division dual (TDD) private network system are introduced. Then, deterministic calculation method is used to study the situation of UAV interference with neighboring frequency broadband TDD private network system, and believe that 1800MHz UAV-mounted base station can realize coexistence with neighboring frequency broadband TDD private network system. When the interference isolation distance between UAV and other systems is up to 0.1km, which is less than the flight altitude of UAV of 4km, no additional isolation distance is needed to achieve coexistence.

5G has the characteristics of ultra-high broadband, low latency reliability, wide coverage, and large connectivity, which combined with network slicing and edge computing capabilities, further expands the application scenarios of unmanned aerial vehicles (UAV). The application of UAVs combined with 5G has already taken off. However, there are many spectrum security issues. Unmanned Aerial Vehicles are under serious threat from scarce spectrum resources and insufficient bandwidth for transmitted data, posing a challenge to the use of spectrum for UAV. The Ministry of Industry and Information Technology (MIIT) has divided the international mobile telecommunications (IMT) frequencies, and the 1765-1785MHz/1860-1880MHz bands are planned for IMT systems in FDD mode. In this paper, we analyze the situation of UAV interference with neighboring frequency broadband time division dual (TDD) private network system by building an interference scenario using deterministic calculation method. After the analysis, we believe that the UAS can achieve coexistence with the neighboring frequency wideband TDD private network system.

Due to the rapid development of the Internet of Things (IoT), many latency-sensitive application businesses have recently emerged, such as the Telematics business. The traditional approach is to reduce latency by offloading tasks to MEC servers to meet the low latency requirements of the business. However, the limited number of MECs is not enough to cover all the roads, and adding MEC devices can greatly increase the equipment cost. In this study, a multi-vehicleassisted MEC system is proposed as a task offloading model for deep reinforcement learning (DRL)-based vehicle edge computing (VEC). The system includes both vehicles with limited computational power and VEC servers with more powerful processing power of roadside units (RSUs). We employ an Actor-Critic based DRL technique to determine when tasks are executed in the local vehicle or offloaded to the RSU unit for execution, expecting to improve the convergence speed of the algorithm while obtaining the lowest latency system performance improvement. Simulation results show that our proposed Actor-Critic based DRL approach can effectively accelerate the convergence speed of the system, improve the system performance, and reduce the overall cost of the system compared to the conventional DQN approach.

Vehicles may produce a lot of data that is timesensitive and computationally intensive due to the quick development of on-board applications. Due to the limitation of vehicle computing power and battery capacity, these data cannot be processed in time. Vehicle edge computing (VEC) is increasingly used to solve this problem because of its greater computing power. This paper proposes a VEC task offloading model based on improved Q-learning algorithm. First of all, we establish the system model. Due to the complexity of the system model environment, we adopted the reinforcement learning (RL) algorithm, but the environment space and action space in RL are large, which will lead to slow convergence of the model. We combine RL and deep learning (DL) to increase convergence efficiency, and to convert the task of maintaining the value function table, we utilize deep reinforcement learning (DRL) by training a neural network model. The revised Q-learning algorithm’s solving procedure is then thoroughly introduced. The simulation outcomes demonstrate that as training times are increased, the training loss of the enhanced Q-learning algorithm gradually declines and tends to converge to zero. It has been confirmed that our suggested approach does a goodjob of evaluating the system cost. As the number of vehicles on the route increases, the system experiences an increase in time cost. We also compare the traditional Q-learning algorithm to the improved Q-learning strategy. The simulation results show that the enhanced Q-learning algorithm is much faster than the conventional Q-learning method.

The rapidly expanding Internet of Vehicles (IoV) poses many challenges, such as the difficulty of providing ubiquitous connectivity and best-in-class services to many vehicles, and the fact that vehicles can generate large amounts of time-sensitive and computationally expensive data. These data cannot be processed in a timely manner because the vehicles lack the computational power of MEC servers due to the limitations of the vehicles’ computational power and battery capacity. In this study, we propose a task offloading method for vehicle edge computing (VEC) based on deep reinforcement learning(DRL), which combines reinforcement learning (RL) with deep learning (DL) and can transfer computationally demanding tasks such as data processing to a VEC server with more processing power, and, we use the Actor-Critic algorithm, which transforms the value function table maintenance into the training of neural network models to improve the convergence efficiency and the training effect of the models. Simulation results show that our proposed Actor-Critic based DRL algorithm may significantly improve the effectiveness of VEC servers and reduce the vehicle cost.

Space-ground integration is a key component of 6G with diverse topologies, time-varying topologies, broad geographical and temporal scales, and limited spatial node resources, bringing new development opportunities and scope for mobile communications. Before designing the 3D 6G space-ground integrated network architecture, we first examine the main difficulties of space-ground integrated networks, and then examine the common use cases and requirements of 6G-oriented space-ground integrated networks. Finally, we propose a space-ground integrated network architecture based on a decentralized core network and a ground-centralized core network. For the former, the core network is placed on the ground while the airborne platform performs certain access network activities. To meet the needs of various application scenarios, the latter airborne platform performs access network operations and loads a portion of the core network components. Dynamic deployment of key network functions becomes possible as the network functions are reconfigured to meet the changing application scenario needs and network operation requirements.

Skills analysis is an interdisciplinary area that studies labor market trends and provides recommendations for developing educational standards and re-skilling efforts. We leverage techniques in this area to develop a scalable approach that identifies and evaluates educational competencies. In this work, we developed a skills extraction algorithm that uses natural language processing and machine learning techniques. We evaluated our algorithm on a labeled dataset and found its performance to be competitive with state-of-the-art methods. Using this algorithm, we analyzed student skills, university course syllabi, and online job postings. Our cross-sector analysis provides an initial landscape of skill needs for specific job titles. Additionally, we conducted a within-sector analysis based on programming jobs, computer science curriculum, and undergraduate students. Our findings suggest that students have a variety of hard skills and soft skills, but they are not necessarily the ones that employers want. The data also suggests these courses teach skills that are somewhat different from industry needs, and there is a lack of emphasis on soft skills. These results provide an initial assessment of the program competencies for a computer science program. Future work includes more data gathering, improving the algorithm, and applying our method to assess additional educational programs.