With the advancement of low-earth orbit (LEO) satellite technologies, the Large-Scale LEO Satellite Networks (LSLSNs) have become the cornerstone of next-generation wireless communication, delivering global coverage with low latency and massive throughput. However, the LSLSNs operate in an open space environment, where the effects of space environmental factors such as electromagnetic radiation, thermal conditions and solar flares can easily lead to regional correlated damage for satellite nodes, severely degrading network performance. To ensure stable and robust transmission, we propose a Robust and Intelligent Multipath QUIC Transmission (RIMT) method in LSLSNs. The RIMT method is deployed in multi-domain based satellite networks leveraging distributed Software-Defined Networking (SDN) technology, where satellites are divided into various autonomous domains managed by a local SDN controller for high efficiency and flexible management. To address regional correlated damage, RIMT employs pre-computed backup flows to seamlessly switch from compromised flows. Additionally, we introduce an innovative congestion control algorithm designed to maintain stable data transmission during the route switching process. We implement RIMT in the Kuiper K3 shell network, experiments show that RIMT can achieve more enhanced performance than other mechanisms.

The ultradense low-Earth orbit (UD-LEO) satellite network has attracted significant attention recently due to its great potential in providing global Internet coverage and services. For the sake of improving performance and reliability, multiple network paths can be utilized for coordinated transmission. However, state-of-the-art multipath routing algorithms face the challenge when dealing with highly dynamic network characteristics (i.e., high-speed node movement, frequent topology changes) in such emerging networks. In this article, we propose a deep-reinforcement-learning-based multipath cooperative routing (DRL-MPCR) scheme for UD-LEO satellite networks, with the aim of enhancing routing discovery capability and improving multipath transmission performance. Two main building blocks of multipath transport protocol are considered: 1) routing discovery and 2) multipath scheduling. On the one hand, in order to cope with the highly dynamic satellite network, a DRL-based multipath routing discovery algorithm is proposed, where satellite agents independently make routing decisions according to the perceived local network state, so that multiple available paths can be obtained. On the other hand, to promptly make traffic scheduling according to the varying path conditions, a water filling algorithm-based multipath scheduling policy is designed, which aims to optimize the maximum path cost when multiple paths are utilized for cooperative transmission. Extensive simulation results demonstrate that the proposed DRL-MPCR scheme achieves more efficient routing discovery and better multipath transmission performance than existing ones.

With the development of low-Earth orbit (LEO) satellites, ultra large-scale LEO satellite networks (ULSLSNs) hold immense potential to provide high-speed and reliable services in future communication systems. It offers substantial promise to meet emerging Internet traffic demands, such as remote real-time applications with stringent delay constraints (deadline). However, the complexity of ULSLSNs is significantly amplified by the satellite mobility, limited bandwidth, and more packet losses. The complex and dynamic network can greatly increase block transmission latency and may further degrade user’s Quality of Experience for real-time applications. To reduce block transmission latency and deliver more blocks before deadline for real-time applications, we propose a quick user datagram protocol Internet connection-enabled robust streaming transmission (QRST) framework deployed in ULSLSNs. This framework comprises four components and mainly involves an adaptive forward erasure coding (FEC) scheme and a deadline-driven block scheduler. The FEC scheme dynamically allocates redundancy based on current network conditions to reduce extra retransmission delay and balance bandwidth overhead. The block scheduler selects blocks for transmission to deliver more blocks before deadline, especially for high-priority blocks. Finally, we implement QRST over the network of Kuiper K3 Shell simulated by NS-3 and living video streaming applications are transmitted. The experiment results show that QRST can significantly enhance the transmission performance of video streaming applications in ULSLSNs compared with other mechanisms.

The booming renaissance and rapid development of ultra-dense low earth orbit (LEO) satellite networks (UD-LSNs) are envisioned to realize a giant leap forward for the future sixth generation (6G) coverage expansion, bridging digital divide for remote areas and providing continuous services for user terminals worldwide. However, the inherent dual mobility, massive access scenarios and highly overlapped coverage may trigger frequent, vast and ping-pong handovers, especially with the existing limited and fixed deployment of terrestrial mobility functional entity. To this end, by exploiting the unique opportunity of UD-LSNs, we devise a medium Earth orbit (MEO) assisted distributed hierarchical mobility management architecture (HDMMA) with flexible function configuration to adapt the high dynamic and large scale network. Subsequently, the lightweight handover procedures (LHPs) are proposed for two scenarios under the HDMMA to ensure service continuity, that is on-orbit handover and off-orbit handover. Considering the user mobility attributes and satellite available resources, the on-orbit handover introduces user aggregate to share signaling overhead, while the off-orbit handover is further classified into intra-cluster, inter-cluster and inter-group handover based on the clustering and grouping. Furthermore, we conduct theoretical analysis model on the proposed LHP in terms of signaling overhead and handover latency. Simulation results verify the handover characteristics in UD-LSNs, illustrate the superiority of our HDMMA and demonstrate the handover performance improvement of the proposed LHP.

Nowadays, the ultra-dense low earth orbit (LEO) satellite network has become an attractive solution for providing global Internet coverage and services. With the ever-increasing demand for higher transmission performance, multipath also attracts much attention due to its great potential. In this paper, we consider the multipath cooperative routing in the ultra-dense LEO satellite network. To cope with the high dynamics of the network environment, a deep reinforcement learning (DRL) empowered intelligent routing algorithm is proposed, where each satellite only observes the local network state and independently makes the next-hop forwarding decision. Meanwhile, the perceived conditions of each path are recorded hop by hop in a format-specific packet. In this way, multiple available paths can be found for cooperative transmission. To balance multipath load, an adaptive traffic scheduling scheme is further developed on the sender to adjust traffic distribution according to the varying path conditions, so that multiple sub-flows can be efficiently maintained. Simulation results show the superiority and the effectiveness of the proposed multipath cooperative routing scheme compared with other baseline schemes.

Ultra-dense low earth orbit (LEO) satellite network (UD-LSN) is one of the most promising architectures in the sixth-generation (6G) systems, providing several types of services with different service level agreements (SLAs). Network slicing technology effectively meets these SLAs by building multiple logical networks isolated from each other on the physical network. In the UD-LSN, due to the spatiotemporal variations of users and available satellites, it poses a considerable challenge to make dynamic slicing decisions individually for each LEO satellite. This paper proposes a two-layer dynamic reconfigurable radio access network (RAN) slicing architecture for the UD-LSN. We consider the characteristics of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (uRLLC) services and formulate a stochastic optimization problem to maximize the long-term slicing utility, which consists of resource utilization, throughput, and reconfiguration cost. The original problem is transformed into a Markov Decision Process (MDP) and solved with the Branch Dueling Q-Network (BDQ)-based dynamic reconfigurable RAN slicing (DRRS) algorithm in a large slicing window and the priority-based user access algorithm in a small time slot. The simulation results validate the effectiveness of the proposed two-layer DRRS strategy, which has a better performance in the slicing utility, resource utilization, and throughput.

As one of the most promising architecture in the evolving sixth-generation (6G) systems, ultra-dense low Earth orbit (LEO) satellite network (UD-LSN) is drawing increasing attention due to its global coverage and ubiquitous access. To ensure service continuity, mobility management with provision of seamless handover is crucial in the process of satellite and user movement. However, massive service requests and overlapped satellite coverage will result in frequent handovers and diversified options in the UD-LSN. Meanwhile, existing mobility management methods based on the terrestrial networks are difficult to make timely and effective decisions due to the limited deployments of ground stations. In light of this, we propose a two-layer grouping and clustering based mobility management architecture (GCMMA) for the UD-LSN to reduce the management complexity with supporting the flexible function configurations. Under the GCMMA, we design lightweight handover procedures for different scenarios according to the established handover model, which considers user aggregation and combines with the regularity of satellite motion. Simulation results validate the effectiveness of the proposed mechanism, which has a better performance in handover delays and signaling overheads.

The Ultra-Dense LEO Satellite Networks (UDLSN) has immense potential to provide low-latency and high-reliability services in future communication networks, owing to its global coverage, high capacity and reliable connectivity. However, the LEO networks usually suffer relatively high and variable transmission errors due to multipath, shadowing and handover. For delay-constrained video transmission, existing packet protection mechanisms frequently violate the constraint and degrade quality in such environments. In this paper, we propose a QUIC-Enabled Reliable Video Transmission Scheme (QRVTS) with adaptive Forward Error Correction (FEC) to reduce loss recovery time and enhance transmission performance especially for delay-constrained videos. Specifically, QRVTS incorporates an adaptive mechanism to dynamically adjust FEC redundancy based on the prevailing channel loss conditions and frame types. We evaluate our mechanism under multiple satellite scenarios with different network characteristics. The simulation shows significant gains in overview completion time and frame-level delivery delay for delay-constrained video transmission in LEO satellite scenarios.

With the rapid development of low earth orbit (LEO) satellite technologies and constellation projects (like Starlink and OneWeb), the ongoing ultradense LEO satellite networks can provide an efficient solution to ubiquitous connection of aircraft. It is urgent to design the ultradense LEO-network-enabled satellite-to-aircraft communication for in-cabin users with high-speed Internet connectivity requirements. In this article, we investigate the resource scheduling of ultradense LEO-network-enabled satellite-to-aircraft communication and the in-cabin communication inside aircraft in civil aviation. First, we adopt the Lyapunov optimization framework to tackle the high dynamic and fast mobility of satellites and aircraft. For the satellite-to-aircraft access and subchannel assignment, we propose a dynamic access and subchannel allocation algorithm based on the matching theory, which can converge to a stable matching within a finite iterations. To guarantee customized needs of in-cabin users, we propose a power control algorithm based on the successive convex approximation technology to optimize the power allocation of satellites on each subchannel and the transmit power of aircraft for each in-cabin user, which is proven to obtain a saddle point or a local minimum with low complexity. Extensive simulations have been carried out to validate the effectiveness of the proposed resource allocation method.

With the emergence of numerous new applications and the explosive growth of Internet of Things (IoT) devices in beyond 5G (B5G) networks, the massive yet delay-sensitive personalized content delivery has imposed a crucial challenge to mobile network operators (MNOs). Cooperation among MNOs for sharing the communication, caching, and computing (3C) 3-D resources in an economic yet real-time manner has provided a promising solution to address this challenge. In this article, we investigate the 3C resource sharing among multiple MNOs to realize efficiently and economically personalized content delivery, where a third-party 3C resource provider (CRP) is introduced to manage the sharing 3C resource pool. By leveraging the multidimensional contract theory, we propose an optimal 3C resource contract scheme for the CRP in a realistic asymmetric information scenario, and the appointed 3C resources in one contract will be allocated to the MNO who signs it. For each MNO, we establish a partial transcoding model to achieve the optimal orchestration on the 3C resources, where the closed-form solution of caching placement and transcoding strategy is obtained. Then, MNOs can choose the most suitable contracts to sign based on the service requirements from end users. In particular, we analyze the global incentive compatibility and feasibility of the proposed multidimensional contract approach, which is theoretically proven to achieve the optimal solution. Extensive simulation results demonstrate the efficiency of the proposed 3C resource sharing mechanism compared with other benchmark schemes. Specifically, the proposed 3C resource sharing scheme can reduce 35% delivery delay compared with the nonsharing scheme.

Two-tier computing paradigm that takes full advantage of both the end-user and the cloud computation capabilities has emerged as a promising way to deal with computationally-intensive tasks in the next generation wireless networks. For promoting the integration of the two-tier computing, federated learning (FL) provides an effective framework to enable the collaboration between the end-user and the cloud. However, the key performance metric, i.e., FL training latency, will be severely affected by the worst wireless link quality in both uplink and downlink. In this paper, aiming at accelerating the FL enabled end-cloud two-tier computing over the wireless networks, we introduce the uplink and downlink fully-decoupled radio access network (FD-RAN) architecture to enhance the minimum wireless link rate via multiple base stations (BSs) access collaboration and power management solution. First, the Lagrange dual decomposition and the binary variable relaxation methods are leveraged to obtain an optimal multiple BS access scheme for the enhancement of minimum uplink and downlink SINR. Subsequently, we exploit the successive convex approximation (SCA) algorithm to deal with the uplink power control and downlink power allocation with a proved data rate lower bound. Furthermore, considering the dynamic channel realizations, a stochastic optimization technique with a convex surrogate function is utilized to find the best end-cloud two-tier computing scheme for FL applications. Simulation results have demonstrated the effectiveness of our proposed joint multiple access collaboration and power management solution over FD-RAN for achieving a faster FL enabled two-tier computing task.

With the rapid expansion of the scale of deployed low earth orbit (LEO) satellites, the ultra-dense LEO satellite-terrestrial integrated network (LTIN) is envisioned as a promising architecture in the sixth-generation (6G) system to implement seamless connectivity and high-speed data rate service. Especially for ultra-remote real-time services with long transmission distance and high delay requirements, the integrated network can guarantee its end-to-end service continuity. However, many challenges have been posed to the efficient resource orchestration for the service delivery, owing to the large scale, heterogeneity and high mobility of the integrated network. For each service, its data needs to go through a series of on-board processing, before being downloaded to the terrestrial network for further applications. To this end, service function chain (SFC), an ordered concatenation of network functions (NFs), is introduced to support service provision. By allocating the constituent NFs over the LTIN, we propose an efficient multiple service delivery scheme to minimize the overall delivery completion latency, while taking into account resource sharing and competition among multiple SFCs. First, we formulate the multiple SFC embedding problem as a noncooperative game that is further proved as the weighted potential game with at least one Nash equilibrium (NE). With the help of the proposed global coordination mechanism, we design two algorithms to obtain the NE. One is the best response (BR) algorithm with faster convergence, while the other is adaptive play (AP) algorithm with more capacity for best solutions. Then, the stochastic learning (SL) algorithm is proposed to adapt to network dynamics and reduce global information exchange. Finally, extensive simulations validate the convergence and effectiveness of the proposed algorithms.

Nowadays, the ultra-dense low earth orbit (LEO) satellite network has become a promising paradigm in the next generation mobile communication network. With the development of content centric communication, multicast technology also attracts much attention. In this paper, we consider the downlink multicast transmission in the ultra-dense LEO satellite network. Multiple LEO satellites provide multicast service for multiple ground user (GU) groups under their coverage, where each GU group requests the same content. To improve the multicast performance, we propose an optimal subchannel allocation and beamforming scheme to maximize the system max-min fair (MMF) capacity of GUs. By leveraging the many-to-many matching model, we obtain the optimal subchannel allocation solution, and we propose a successive convex approximation (SCA) based algorithm for the downlink beamforming in the matching process. The many-to-many matching algorithm is convergent to a stable solution after finite iterations. Simulation results show the superiority and the effectiveness of the proposed subchannel allocation and beamforming method compared with other baseline schemes.

Recently, the rapid-developed mega low earth orbit (LEO) satellite constellation has shown its great potential in cooperating with terrestrial networks to provide seamless global connectivity and high-speed data rate services. However, the heterogeneity of physical resources and diversity of service demands pose challenges for delivering service in an efficient way in the ultra-dense LEO satellite-terrestrial integrated networks (LTIN). When implementing service delivery, service data generally needs a series of on-board processing and then downloading to the terrestrial network for further applications. In this paper, we introduce service function chain (SFC), a sequence of network functions, to process the data on board and propose an efficient multiple service delivery scheme in the LTIN to minimize the total delivery completion time. Considering the heterogeneous resource sharing and competition among multiple SFCs, we formulate the problem as a noncooperative game, which is further proved as a weighted potential game. We design an improved response (IR) algorithm with fast convergence and an adaptive play (AP) algorithm to find the best Nash equilibrium (NE). Extensive simulation results validate the convergence and effectiveness of the proposed algorithms.

Mobile edge computing (MEC) has been widely considered as an effective technology to handle computationally intensive tasks generated by mobile devices. However, the computation resources at an edge node is usually several orders of magnitude smaller than that of a cloud. Thus, it is rather vital to take an investigation into the collaboration between the cloud and the edge. In this paper, to fully exploit the computation power of the cloud server and achieve energy efficient task offloading, we propose an LEO-assisted terrestrial-satellite network (TSN) architecture for cloud-edge collaborative computation offloading. We formulate the collaborative cloud-edge computing problem that minimizes the energy consumption of the whole TSN under the quality-of-service (QoS) constraints. The optimization problem is further decomposed into two subproblems which are solved by deep neural networks (DNN) and successive convex approximation (SCA) algorithm, respectively. Simulation results show the effectiveness of our proposed cloud-edge collaborative computation offloading architecture on achieving a lower energy cost.

The significant advances in wireless communication together with edge intelligent (EI) technology have facilitated the decentralized edge computing paradigm for data-intensive and delay-sensitive solution on massive Internet of Things (IoT) devices. In this paper, a Cybertwin assisted asynchronous federated learning (AFL) mechanism is proposed for realizing efficient edge computing by taking full advantage of local computation capability under heterogeneous wireless environment. First, Cybertwin is introduced as intermediary communication assistant to coordinate individual model aggregation between the users and the cloud server under AFL training process. Second, for the sake of flexible and effective utilization of communication-computation resources for edge computing, Cybertwin plays the role of intelligent agent to jointly take the local computing and up-link transmission into consideration. A resource optimization problem considering the diversified computing power, varied data size, and available communication bandwidth is formulated and we leverage the block coordinate descent (BCD) method to obtain optimal resource management solution. Extensive simulations are conducted to demonstrate the effectiveness of our proposed Cybertwin assisted AFL mechanism, which can shed further light on the application of data-intensive edge computing paradigm over wireless communication network.

Recent advancements of Vehicle-to-Everything (V2X) communication combined with artificial intelligence (AI) technologies have shown enormous potentials for improving traffic management efficiency and intelligence. To provide innovative and effective data-driven traffic management solution for the coming automated vehicle era, we present a vehicle-road collaboration-enabled nonsignalized intersection management architecture in this paper. First, by dividing the intersection zone into the central section (CS) and the waiting section (WS), a vehicle regulation scheme involved with communication and computation planes is developed for V2X-enabled nonsignalized intersection management. Specifically, in order to guarantee vehicle safety, the definition of no overlapping occupation time in CS and the fastest crossing time point (FCTP) algorithm are employed for vehicle collision avoidance. Second, considering the relative coordination between adjacent intersections, a multiagent-based deep reinforcement learning scheduling (MA-DRLS) algorithm is proposed to realize cooperative multiple intersection management. Through information exchange with different intersection agents, each agent can obtain an optimal scheduling strategy using independent deep reinforcement learning (DRL) network. The features of fixed Q-targets and experience replay are leveraged to improve the reliability of neural network during the training process. Finally, simulation performances in terms of intersection throughput and vehicle waiting time have been provided to validate the effectiveness and demonstrate the superiority of the proposed nonsignalized intersection management solution.

With the advance of unmanned aerial vehicles (UAVs) and low earth orbit (LEO) satellites, the integration of space, air and ground networks has become a potential solution to the beyond fifth generation (B5G) Internet of remote things (IoRT) networks. However, due to the network heterogeneity and the high mobility of UAVs and LEOs, how to design an efficient UAV-LEO integrated data collection scheme without infrastructure support is very challenging. In this paper, we investigate the resource allocation problem for a two-hop uplink UAV-LEO integrated data collection for the B5G IoRT networks, where numerous UAVs gather data from IoT devices and transmit the IoT data to LEO satellites. In order to maximize the data gathering efficiency in the IoT-UAV data gathering process, we study the bandwidth allocation of IoT devices and the 3-dimensional (3D) trajectory design of UAVs. In the UAV-LEO data transmission process, we jointly optimize the transmit powers of UAVs and the selections of LEO satellites for the total uploaded data amount and the energy consumption of UAVs. Considering the relay role and the cache capacity limitations of UAVs, we merge the optimizations of IoT-UAV data gathering and UAV-LEO data transmission into an integrated optimization problem, which is solved with the aid of the successive convex approximation (SCA) and the block coordinate descent (BCD) techniques. Simulation results demonstrate that the proposed scheme achieves better performance than the benchmark algorithms in terms of both energy consumption and total upload data amount.

With a massive number of Internet-of-Things (IoT) devices connecting with the Internet via 5G or beyond 5G (B5G) wireless networks, how to support massive access for coexisting cellular users and IoT devices with quality-of-service (QoS) guarantees over limited radio spectrum is one of the main challenges. In this paper, we investigate the multi-operator dynamic spectrum sharing problem to support the coexistence of rate guaranteed cellular users and massive IoT devices. For the spectrum sharing among mobile network operators (MNOs), we introduce a wireless spectrum provider (WSP) to make spectrum trading with MNOs through the Stackelberg pricing game. This framework is inspired by the active radio access network (RAN) sharing architecture of 3GPP, which is regarded as a promising solution for MNOs to improve the resource utilization and reduce deployment and operation cost. For the coexistence of cellular users and IoT devices under each MNO, we propose the coexisting access rules to ensure their QoS and the priority of cellular users. In particular, we prove the uniqueness of the Stackelberg equilibrium (SE) solution, which can maximize the payoffs of MNOs and WSP simultaneously. Moreover, we propose an iterative algorithm for the Stackelberg pricing game, which is proved to achieve the unique SE solution. Extensive numerical simulations demonstrate that, the payoffs of WSP and MNOs are maximized and the SE solution can be reached. Meanwhile, the proposed multi-operator dynamic spectrum sharing algorithm can support more than almost 40% IoT devices compared with the existing no-sharing method, and the gap is less than about 10% compared with the exhaustive method.

The significant enhancement of vehicular communications together with artificial intelligence (AI) have opened up new horizons for innovative data-driven traffic management solution within intelligent transportation system (ITS). In this paper, to alleviate progressively worse urban traffic, we propose an efficient vehicle-to-everything (V2X) communications enabled non-signalized intersection management framework for automated vehicles. First, a resource reservation model involved with different functional planes has been developed for vehicle collision avoidance in V2X enabled non-signalized intersections. Second, reinforcement learning (RL) solution is leveraged to enhance management efficiency of non-signalized scheduling. Furthermore, considering the dimensionality disaster problem of vehicle state caused by complicated traffic environment, we propose a function approximation based non-signalized intersection control (FA-NIC) algorithm to obtain optimal scheduling strategy. Simulation results are provided to demonstrate the effectiveness of our proposed non-signalized intersection management solution.

With the significant advance of Internet of Vehicles (IoV), a massive number of vehicular users will connect with the Internet via 5G and the beyond 5G (B5G) networks, which will further worse the spectrum scarcity problem in 5G/B5G networks. Therefore, how to enable dynamical and efficient spectrum resource sharing for IoV users is imperative. To this end, we investigate an evolutionary game enabled spectrum sharing blockchain framework for IoV. Specifically, We use alliance nodes and blockchain framework to assist in the completion of multi-WSP spectrum resource allocation, sharing, and the allocation results storage. We first propose an evolutionary game scheme to allocate the spectrum resource among different WSPs. To reflect the vehicle users' communication demand more appropriately, the vehicle mobility is taken into consideration when defining the payoff of the vehicle users. After completing the spectrum allocation, we illustrate the process of allocation results recording and block generation in our established blockchain verification system, where the transactions ledger of spectrum allocation will be stored in each alliance node in a distributed way. Numerical results exhibit the fast convergence speed of the spectrum allocation approach. Moreover, simulations results from our established hyperledger platform validate the effectiveness and implementability of the investigated spectrum sharing blockchain framework.

For the massive access of Internet-of-Things (IoT) devices in 5G or beyond 5G (B5G) wireless networks, how to support the coexisting of cellular users and massive IoT devices with quality-of-service (QoS) guarantees over limited spectrum is challenging. In this paper, inspired by the active radio access network (RAN) sharing standard of 3GPP, we present a heterogeneous multi-operator spectrum sharing architecture by leveraging the spectrum trading to support the coexistence of QoS-guaranteed cellular users and massive IoT devices with non-orthogonal multiple access (NOMA). In the architecture, we formulate the spectrum trading between the wireless spectrum provider (WSP) and mobile network operators (MNOs) as a Stackelberg pricing game. Meanwhile, for each MNO, the cellular users and massive IoT devices can be both serviced in the uplink NOMA networks with QoS guarantees. For the pricing game, we prove the uniqueness of the equilibrium solution, which can maximize the payoffs of MNOs and WSP simultaneously. Moreover, we propose an iterative pricing algorithm to achieve the equilibrium solution with theoretical optimality guarantees. Simulation results demonstrate that our framework can support more IoT devices and reach the optimal spectral bandwidth price.

Recent advancement of connected vehicles technologies combined with machine learning (MA) methods has shown great potential for the improvement of efficiency of Intelligent Transportation System. In this work, considering the spatio-temporal correlations under vehicle distribution on urban road network, neural network based deep learning solution is adopted to obtain vehicle driving characteristics and predict future traffic conditions. First, to address the huge challenge brought by complex traffic environment, we present a fine-grained regional-level forecast structure for the prediction of traffic flow at each road. After that, a residual network based deep learning traffic prediction algorithm called DST-RGTP is proposed for the performance enhancement of vehicle regulation in the entire traffic system. Finally, we use the real traffic data of Beijing and open-source road network data on Openstreetmap to test the proposed method. Simulation results verify the accuracy of prediction approach DST-RGTP, which can help to improve the urban traffic management efficiency.

With the significant advance of vehicle-to-everything (V2X) techniques, unsignalized intersection coordination has been widely recognized to facilitate the development of automated vehicles (AVs) for the intelligent transportation system. However, how to guarantee driving safety while improving the unsignalized intersection management efficiency is a challenging issue. In this article, we investigate the collision-free and efficient V2X-enabled AV scheduling problem at unsignalized intersections. First, by dividing the intersection zone into different collision sections (CSs), we formulate the intersection collision-free model into an absolute value programming (AVP) problem, which is proved to be NP-hard. We consider both nonplatoon and platoon traffic scenarios, and unlike previous algorithms, which require to control all the AVs at each scheduling step with computational intractability, our scheduling algorithm can assign a feasible time for each arriving AV with low complexity. Further, we propose an alternately iterative descent method (AIDM) to solve the AVP problem by assigning the optimal entering time for each arriving AV. Through extensive simulations with various traffic data generated by SUMO, we demonstrate that our proposed AIDM algorithm can significantly enhance the scheduling performance in terms of passing delay and scheduling throughput. Even though the AIDM algorithm achieves the same level of transportation performances with the state-of-the-art algorithm, it advances dramatically in computational complexity and communication overhead, which is easier to be implemented in practice.