Vehicular cooperative perception (VCP) facilitates the exchange of sensing data among vehicles through vehicle-to-everything (V2X) communication, significantly increasing the sensing range and precision of individual autonomous vehicles (AVs). However, efficiently managing the sharing and processing of large volumes of sensing data presents challenges due to restricted communication and computation resources. This study introduces an integrated sensing, communication, and computation (ISCC)-based task offloading and resource allocation (ITORA) framework, which optimizes cooperative perception by determining what data to share, which vehicles to involve, and how to process the data effectively. We develop an information value function to evaluate the data quality for each vehicle. Subsequently, we design strategies for sensing task allocation, task offloading, and resource allocation to enable value-driven data selection at a subregion level, facilitating collaborative computing among edge servers and vehicles. Additionally, we formulate an optimization problem aimed at maximizing information value while minimizing delay and energy consumption, subject to constraints on a full region of interest (RoI) coverage, delay, wireless bandwidth, and computational resources. We decompose the mixed-integer nonlinear programming (MINLP) problem into two subproblems, devising a sensing task allocation algorithm and a proximal policy optimization (PPO)-based task offloading and resource allocation (PTORA) algorithm to address them. Comprehensive simulations validate the effectiveness of the proposed PTORA in optimizing information value, reducing task execution delay, and minimizing energy consumption.

As 6G space-terrestrial integrated vehicular networks (STIVNs) technology advances, vehicles can efficiently complete a variety of computationally intensive tasks by offloading them to various infrastructures (i.e., satellites and base stations). This allows vehicles to make use of the computational resources available in these infrastructures. Nevertheless, traditional task offloading schemes often restrict vehicles to offloading tasks solely to the infrastructure within their immediate communication coverage. This oversight disregards the potential availability of resources from infrastructures beyond. Consequently, this would result in the actual demands of vehicles being limited by the currently connected infrastructure. Furthermore, areas with high vehicle density within the coverage range may face resource inadequacies. Conversely, regions with low vehicle density within the coverage range may experience underutilization of resources. To address the aforementioned challenges, this paper proposes an on-demand task offloading scheme in STIVNs. This scheme aims to fully leverage the mobility of vehicles and multi-hop transmission to effectively utilize computing resources across different infrastructures. Specifically, we comprehensively consider the resources of diverse infrastructures and the density of vehicles within their coverage ranges to provide task offloading services to vehicles with differentiated demands. By integrating vehicle mobility, multi-hop transmission, and matching games, we aim to select the optimal offloading strategy for each vehicle. Simulation results confirm that the proposed scheme outperforms the other three traditional schemes in terms of service satisfaction rate and utility of vehicles.

Preemptive scheduling efficiently addresses the coexistence of enhanced Mobile Broad Band (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC). While URLLC puncturing influences eMBB performance, further investigation is necessary to study the trade-offs between stability, delay, and efficiency. However, existing studies overlook the imbalance in eMBB/URLLC load distribution and personalized fluctuations in eMBB performance, leading to sub-optimal results. To tackle this, we propose an unmanned aerial vehicle (UAV) relay-assisted eMBB/URLLC multiplexing framework. Specifically, considering the utilization of UAVs for connecting separated next-generation Node Bs (gNBs) and the individual subject experience of services, we first formulate the multiplexing problem as an optimization problem. The objective is to maximize eMBB throughput and minimize personalized fluctuations in eMBB performance and UAV consumption, subject to URLLC constraints. Then, the challenging problem is decomposed into the eMBB problem and the URLLC problem. For the former, we further decompose it into three sub-problems and solve them using optimization methods. For the latter, we propose a deep reinforcement learning-based algorithm to obtain an optimal strategy for relaying and puncturing URLLC into eMBB intelligently. Simulation results demonstrate that our proposals outperform benchmark schemes regarding eMBB throughput, UAV consumption, eMBB performance fluctuation, URLLC satisfaction, and learning efficiency.

Unmanned aerial vehicle (UAV) relay networks with flexible and controllable characteristics are expected to complement the capacity of the gNB. This paper studies the multiplexing of enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) in a multi-UAV relay network, where the strict latency requirement of URLLC can be achieved by the preemptive multiplexing of eMBB resources. However, this may affect eMBB reliability due to the transmission interruptions. Moreover, given the limited energy resources of UAVs, there is an inherent tradeoff among reliability, delay, spectral efficiency, and energy efficiency. To address these challenges, this paper develops a hierarchical UAV-assisted eMBB/URLLC multiplexing scheduling framework. For the eMBB scheduler, we first utilize multiple UAVs to assist the gNB in relaying eMBB traffic and formulate the eMBB resource allocation problem as an optimization problem. Then, we propose a decomposition-relaxation-optimization algorithm to maximize eMBB data rates while considering the personalized fairness of resource allocation and UAV power consumption. For the URLLC scheduler, we further consider the multiplexing of eMBB/URLLC traffic based on the optimization of eMBB resources. To reduce the performance fluctuations of eMBB, we propose a novel cross-slot strategy to schedule URLLC within two time slots rather than one time slot as in existing works. With this strategy, a deep reinforcement learning-based algorithm is proposed to obtain the optimal strategy for the preemption of URLLC on eMBB. Simulation results show that the proposed algorithms outperform the benchmark schemes in terms of convergence rate, eMBB reliability, personalized resource fairness, UAV consumption, and URLLC satisfaction.

In vehicular edge computing networks, the real-time, on-demand scheduling of scarce network resources for environmental perception, task offloading, computation, and feedback is vital. However, these coupled processes make resource allocation challenging. Moreover, existing real-time channel measurement techniques in complex vehicular topologies present load, accuracy, and customization difficulties. To address these issues, this paper proposes an on-demand environmental perception and resource allocation strategy. Specifically, with the introduction of a channel knowledge base, we first analyze the coupling relationship between environmental perception, communication, and computation. A model is then proposed for task offloading to schedule the granularity of environment perception, communication resources, and computational resources dynamically. Subsequently, the resource allocation problem is formulated as an optimization problem, aiming to minimize system processing delay and maximize resource utilization while ensuring perception accuracy. To address this, a two-phase optimization-assisted deep reinforcement learning (DRL) algorithm is proposed. The initial phase uses convex optimization to approximate a solution. The second phase proposes a DRL-based algorithm to intelligently schedule dynamic network resources, with the first phase’s solution guiding the initial exploration space to enhance DRL training efficiency. Extensive simulation experiments verify the effectiveness of our proposal.

The combination of integrated sensing and communication (ISAC) with mobile edge computing (MEC) enhances the overall safety and efficiency for vehicle to everything (V2X) system. However, existing works have not considered the potential impacts on base station (BS) sensing performance when users offload their computational tasks via uplink. This could leave insufficient resources allocated to the sensing tasks, resulting in low sensing performance. To address this issue, we propose a cooperative power, bandwidth and computation resource allocation (RA) scheme in this paper, maximizing the overall utility of Cramér-Rao Bound (CRB) for sensing accuracy, computation latency for processing sensing information, and communication and computation latency for computational tasks. To solve the RA problem, twin delayed deep deterministic policy gradient (TD3) algorithm is adopted to explore and obtain the effective solution of the RA problem. Furthermore, we investigate the performance tradeoff between sensing accuracy and summation of communication latency and computation latency for computational tasks, as well as the relationship between computation latency for processing sensing information and that of computational tasks by numerical simulations. Simulation demonstrates that compared to other benchmark methods, TD3 achieves an average utility improvement of 97.11% and 27.90% in terms of the maximum summation of communication latency and computation latency for computational tasks and improves 3.60 and 1.04 times regarding the maximum computation latency for processing sensing information.

Air-ground Integrated Mobility (AIM) can effectively alleviate the current urban traffic pressure by expanding transportation resources in the near-ground field. However, the following problems in AIM need to be addressed urgently: 1) The high mobility of Personal Aerial Vehicles (PAVs) in low-altitude airspace leads to a sharp increase in risk factors; 2) Due to the limited communication distance, antenna direction angle, and frequent handover caused by high-speed movement, the communication quality in the air is unreliable; 3) AIM incorporates vehicles on the ground and PAVs in the air leading to the high variability of user requirements. Confronted with the personalized resource requirements of high-speed mobile PAVs in airspace with unreliable communication quality, traditional resource allocation strategies struggle to guarantee service quality. Therefore, we propose a safety-oriented personalized resource allocation strategy in AIM, which jointly considers the user requirements and resource distribution. Specifically, we first build a three-dimensional (3D) safety distance model by analyzing the motion process of PAVs with the help of a kinematics model. Then, according to the location, speed, and environmental information of the PAVs, the communication and computing resources required by each PAV under the premise of maintaining the optimal safety distance are obtained through the transmission model. Furthermore, the 3D safety distance and resources are jointly optimized, and an on-demand resource allocation algorithm enabled by Deep R?einforcement Learning (DRL) is constructed to provide the resource allocation strategy based on the personalized requirements of the users.

In digital twins (DT) enabled vehicular networks, vehicles need to comprehensively consider their different synchronization requirements of data and the resource status of available networks to map parameters to the DTs deployed in the cloud during the driving process. Therefore, how to develop the optimal data synchronization frequency and the ratio of synchronized data volume for each vehicle to maximize its utility has become a key challenge in the data synchronization process. In this article, we propose an on-demand data synchronization scheme for differentiated DTs to obtain the optimal data synchronization strategy between each vehicle and its DT. Specifically, the data synchronization requirement of each vehicle is considered to design a dynamic data synchronization optimization problem for vehicles. Furthermore, the optimization problem is modeled as a Markov decision process and an on-demand data synchronization algorithm based on twin delayed deep deterministic policy gradient (TD3) is designed to obtain the optimal data synchronization frequency and optimal synchronization data ratio for each vehicle. Simulation results show that our scheme can achieve the highest reward compared to conventional schemes.

In digital twins enabled space-air-ground integrated networks (DT-SAGINs), the DT of a vehicle (DT-V) needs to constantly migrate between the infrastructures deployed on the path of the vehicle as the vehicle moves to provide stable and continuous driving services for the vehicle. However, each DT-V has differentiated migration requirements and the heterogeneous network infrastructures have various migration performances. Therefore, how to design a scheme that jointly considers the above factors to determine the optimal migration strategy for each DT-V becomes a challenge. In this paper, we propose a game-based migration scheme for the DT-Vs in DT-SAGINs. In this scheme, we first design a two-layer DT migration architecture, where each vehicle has two DTs and each network infrastructure only has one DT. The two DTs of the vehicle are respectively deployed in the cloud layer (Primary DT-V) and the edge layer (Second DT-V). In contrast, the DT of each network infrastructure is deployed in the cloud layer (DT-I). Based on the designed architecture, the interaction of the Primary DT-Vs and the DT-Is deployed in the cloud layer is formulated as a matching game, where an integrated algorithm that couples bilateral matching and dynamic programming is designed to obtain the optimal migration strategy for each Second DT-V deployed in the edge layer to maximize its average utility. The simulation results show that the proposed scheme can lead to a higher utility for each Second DT-V than the conventional schemes.

The digital twins (DT) empowered space-air-ground integrated vehicular networks (SAGIVN) can efficiently manage data of nodes and provide cross time interactive decisions for nodes, thus significantly enhancing the network service capability. In DT empowered SAGIVN (DT-SAGIVN), vehicles need to comprehensively consider their data synchronization requirements and the resource status of heterogeneous networks to update new parameters to their DTs deployed in the cloud. Therefore, how to continuously access a group of optimal networks in the process of driving from the origin to the destination to complete data synchronization while maximizing the utility of each vehicle has become a key challenge. To solve this problem, we propose a dynamic data synchronization (DDS) scheme in DT-SAGIVN. In this scheme, we first establish a DT empowered network model and a communication model in heterogeneous networks. Then, by considering the dynamic wireless networks, the mobility of vehicles and the characteristics of heterogeneous links, the vehicle data synchronization optimization problem is modeled as a Markov decision process (MDP). After that, based on the vehicle's personal preference for cost and delay, we propose a DQN-based method to solve the MDP problem, so as to make the optimal decision for each vehicle to complete data synchronization. Simulation results show that the proposed scheme can bring the highest utilities for vehicles compared with the traditional schemes.

Urban air mobility (UAM) provides a new solution to relieve urban transportation pressure by expanding transportation resources of near-ground space. The vigorous development of emerging technologies such as artificial intelligence, intelligent transportation, and sixth-generation (6G) communication technologies have greatly promoted the progress of UAM. However, UAM also increases traffic safety hazards while introducing vertical dimension transportation resources. Traditional collision avoidance is not suitable for three-dimensional (3-D) air-ground integrated mobility scenario, which considers safety hazards in vertical dimensions as well as the resource supply and demand conflict due to the combined effect of directional antenna angle and limited communication distance. Therefore, a safety-oriented on-demand resource allocation strategy for air-ground integrated mobility is proposed. Specifically, we first model the 3-D safety distance model in the air-ground integrated mobility scenario and construct its quantitative relationship with communication and computing resources. Secondly, a 3-D safety distance optimization model is proposed with joint consideration of safety-oriented resource requirements and resource distribution, which can allocate resources in the scenario. Furthermore, a 3-D safety distance optimization algorithm based on deep reinforcement learning (DRL) is designed for solving the optimization model, which implements a safety-oriented resource allocation. Simulation results show that the proposed safety control strategy can effectively improve the safety of air-ground integrated mobility and alleviate the contradiction between the supply and demand of resources.

The fifth-generation network (5G) has made great progress. With the continuous development of communication technology, by analyzing the characteristics of 5G scenarios, the sixth-generation network (6G) technology combined with multiple scenarios provides effective solutions for the implementation of emerging services with stringent requirements. It is worth noting that the vigorous development of emerging services has been weakened due to the limited resources provided by a single scenario, cross-scenario technologies are urgently needed to enable emerging services in the 6G stage. However, most of the existing work only focuses on a single scenario, which leads to emerging services with complex requirements still difficult to achieve in practice. Therefore, we propose an efficient representation and scheduling framework to achieve the unification of cross-scenario resources, aiming to solve the problem of resource scheduling in cross-scenario. In the above framework, first of all, considering the strict resource requirements of emerging services, we establish a unified resource representation model based on the Time-Expanded Graph (TEG). Secondly, to maximize resource utilization, based on the representation model, a cross-scenario resource scheduling model is proposed. Then, considering the complexity of solving the scheduling model, a resource utilization maximization strategy is presented through the primal decomposition. Simulation results show that the unified framework can effectively improve resource allocation efficiency in complex 6G scenarios.

通过集成全域网络资源及普适智能，6G网络有望为用户提供泛在的个性化服务。传统资源调配方案通常基于已知场景或普适场景，无法真正实现服务的按需响应。因此，首先分析用户主观需求及客观环境需求对按需资源调配的影响。其次，设计一个集成主观用户需求和客观环境需求的6G通感算资源按需调配架构。最后，讨论分析架构特点及可行性，为未来6G网络资源按需调配研究的发展和应用提供参考。

By building a road information sharing system at the edge, namely EdgeSharing, the traffic safety problems caused by the perception blind spots of self-driving vehicles can be effectively alleviated. EdgeSharing is inherently a crowdsourcing system where sensory data are firstly uploaded from vehicles and then analyzed at the edge to maintain information updating. As such, uploading latency and analysis accuracy are two imperative components to measure the performance of EdgeSharing system, which however challenges the traditional Internet of Vehicles (IoV) that have no consideration for the diverse quality requirements of the content of sensory data. In this paper, we take video captured by on-board camera as an example and devise an adaptive configuration strategy for effectively data uploading and analysis at the edge. Naturally, configuration parameters affect system performance in an unknown and time-varying fashion because of the varying video content and network conditions. To address that, we adopt a Bayesian online learning method that learns the optimal configuration timely by observing historical system performance and real-time environmental information. Our strategy is able to maximize the data analysis accuracy at the edge under the constraint of desired frame rate, which makes EdgeSharing system accommodate complex vehicular environments. Simulation results indicate that compared to other schemes, our proposal finds the optimal configuration fastest and results in about 50% regret reduction.

In 5G/B5G networks, the preemptive scheduling provides an efficient solution to the coexistence problem of eMBB/URLLC services. Current works usually assume that the downlink transmission duration of each URLLC service is within one mini-slot, which ignores the different requirements of URLLC users and may lead to the severe data rate loss of eMBB services and low resource utilization efficiency. To deal with above problem, we propose a novel URLLC preemptive strategy, where the arriving URLLC services could cross through multiple mini-slots rather than only one to puncture resources on demand. With the proposed strategy, considering the heterogeneous delay requirements of URLLC services and the preemptive influence on eMBB services, an efficient algorithm based on optimized sparrow search is also proposed. Through allocating time and frequency resources occupied by each URLLC service on de-mand, the number of URLLC services supported by the gNB is maximized while the satisfaction of eMBB services is ensured. The simulation results indicate that the proposed algorithm can achieve better performance compared with the benchmark schemes.