Cellular vehicle-to-everything (C-V2X) communication is essential for supporting diverse vehicle applications in cooperative vehicle-infrastructure systems (CVISs). However, various factors, such as building obstructions, signal blockages, and network conditions, severely affect the latency and reliability of V2V communication. To address this challenge, we propose a novel dynamic method for communication mode selection and resource allocation in C-V2X, termed MSRA, which enables vehicles to dynamically select and utilize different communication modes, such as V2V, Vehicle-to-Infrastructure (V2I), and Vehicle-to-Network (V2N) for direct, relay, or forwarding communication, based on real-time conditions. To this end, we formulate the joint optimization problem of mode selection and resource allocation as a Markov decision process (MDP) and propose a solution based on multiagent reinforcement learning (MARL) with centralized training and decentralized execution. Specifically, each vehicle acts as an agent, independently selecting communication modes and resources based on real-time network status and communication link quality, aiming to satisfy the latency and reliability requirements of V2V communication while maximizing the capacity of V2N communication based on heterogeneous Quality-of-Service (QoS) requirements. Simulation results indicate that the proposed algorithm significantly outperforms other decentralized baselines and demonstrates superior performance under various conditions.

Cellular vehicle-to-everything (C-V2X) communication is a crucial fundamental technology to serve diverse vehicular applications. However, the diversity of communications services in vehicular networks poses a challenge for designing an intelligent and efficient resource allocation framework. In this paper, a deep reinforcement learning (DRL)-based resource allocation framework is developed to meet the heterogeneous Quality of Service (QoS) requirements in heterogeneous vehicular communications networks, by training an agent to jointly optimize the sub-band and transmission power allocation. In particular, the resource allocation framework is formulated by considering the requirements of two key communication modes, the latency and reliability requirements of direct communication mode and the high-capacity requirements of cellular communication mode. Moreover, the interference of fast channel variations in high mobility vehicular environments is fully considered and modeled. Simulation results show that the proposed algorithm outperforms other baseline algorithms while achieving near-optimal performance.

The jamming and interference in vehicular ad hoc networks (VANETs) depend on the channel states of vehicles from the ambient radio transmitter, which in turn result from the topologies and radio features. In this paper, we propose an environment-aware reinforcement learning (RL)-based VANET communication scheme against jamming and interference that applies the post decision state algorithm to optimize the power allocation and channel selection without relying on the jamming attack model. This scheme exploits the environment information in the state formulation due to the traffic density and their locations reflect the interference level, as well as the location of transmission vehicle combined with building structure and heights indicate the channel gain and shadowing. The proposed post decision state-based RL method employs the estimated future communication distances of the moving vehicles to accelerate the learning process. We provide the performance bounds of the energy consumption, bit error rate (BER), and utility based on a Nash equilibrium. Simulation results show that the proposed scheme significantly reduces the BER with less energy consumption compared with the benchmark.

New Radio-Vehicle-to-Everything (NR-V2X) is motivated for supporting the advanced V2X applications, such as the autonomous driving, platooning, etc. The enhanced sidelink resource reservation mechanism of NR-V2X utilizes the characteristics of V2X applications, Hybrid Automatic Repeat reQuest (HARQ) scheme to improve the extreme high reliability and provide the flexible resource indications. Meanwhile, the sidelink signaling overhead for sidelink resource reservation and system performance gain should be balanced carefully. In this paper, the challenges and detailed design issues of the enhanced sidelink resource reservation mechanism of NR-V2X are depicted. The performances with typical configurations and scenarios are presented and analyzed.

Fog radio access networks (F-RANs), in which the fog access points are equipped with communication, caching, and computing functionalities, have been anticipated as a promising architecture for enabling virtual reality (VR) applications in wireless networks. Although extensive research efforts have been devoted to designing efficient resource allocation strategies for realizing successful mobile VR delivery in downlink, the equally important resource allocation problem of mobile VR delivery in uplink has so far drawn little attention. In this work, we investigate a mobile VR F-RAN delivery framework, where both the uplink and downlink transmissions are considered. We first characterize the round-trip latency of the system, which reveals its dependence on the communication, caching, and computation resource allocations. Based on this information, we propose a simple yet efficient algorithm to minimize the round-trip latency, while satisfying the practical constraints on caching, computation capability, and transmission capacity in the uplink and downlink. Numerical results show that our proposed algorithm can effectively reduce the round-trip latency compared with various baselines, and the impacts of communication, caching, and computing resources on latency performance are illustrated.

Vehicle-to-everything (V2X) can improve the road safety, the traffic efficiency, and the availability of infotainment services. In order to meet the requirement of better coverage, higher reliability and low latency for 5G New Radio (NR)-V2X, the new design of sidelink synchronization signals were introduced in 3GPP Release 16. The enhanced synchronization signals for sidelink were studied, to support UEs to extend its coverage range of sidelink synchronization signals and to mitigate the frequency error. The performance evaluations results show that length-255 m-sequence and multiple hypotheses method proposed in this paper can improve the detection probability of sidelink synchronization signals.

High definition map (HDM) is essential to autonomous driving vehicles for path planning and driving decision-making. However, the data of HDM is quite different from other applications. HDM data has the characteristics such as large amount of basic data, frequent updating with small volume, accurate geolocation, etc. In this paper, aiming to reduce the energy consumption and offloading delay, we propose a cluster-based strategy for HDM data offloading by combining the characteristics of HDM data and the mobility of vehicles. Simulation results show that the proposed strategy performs well in the energy consumption and delay.

Vehicle-to-everything (V2X) can improve the road safety, the traffic efficiency, and the availability of infotainment services. Standardization of New Radio (NR) V2X has been completed preliminary by the Third Generation Partnership Project (3GPP) in Nov. 2019. This paper mainly focuses on the Automatic Gain Control (AGC) designing issues for the physical channels in NR-V2X. The sequence-based AGC-Reference Signal (RS) mechanism is introduced for AGC training and frequency-offset estimation caused by Doppler effects. For a sequence-based channel, the structure of none-AGC symbol is proposed. The related simulation results are provided to show the proposed method can improve the reliability without wasting any physical resources.

Cellular vehicle-to-everything (C-V2X) is an important enabling technology for autonomous driving and intelligent transportation systems. It evolves from long-term evolution (LTE)-V2X to new radio (NR)-V2X, which will coexist and be complementary with each other to provide low-latency, high-reliability, and high-throughput communications for various C-V2X applications. In this article, a vision of C-V2X is presented. The requirements of the basic road safety and advanced applications, the architecture, the key technologies, and the standards of C-V2X are introduced, highlighting the technical evolution path from LTE-V2X to NR-V2X. Especially, based on the continual and active promotion of C-V2X research, field testing, and development in China, the related works and progresses are also presented. Finally, the trends of C-V2X applications with technical challenges are envisioned.

Vehicle-to-everything (V2X) can improve the road safety, the traffic efficiency, and the availability of infotainment services. In order to meet the requirement of higher data rate for Long Term Evolution (LTE)-V2X, the carrier aggregation scheme was introduced in 3GPP Release 15. The enhanced resource selection mechanism for sidelink multi-carrier was studied, to support UEs with limited transmission capability performing multi- carrier operations and to alleviate the half- duplex impact. The system level simulation results show that the half-duplex impact can be suppressed and the proposed mechanisms in this paper can improve the reliability of LTE-V2X sidelink multi- carrier operation.

Vehicle-to-everything (V2X) can improve the road safety, the traffic efficiency, and the availability of infotainment services. Standardization of Long Term Evolution (LTE)-V2X has been actively conducted by the Third Generation Partnership Project (3GPP) to provide the solutions for V2X. In this paper, the challenges and detailed design issues in LTE V2X are discussed. The performance comparison of LTE-V2X and IEEE 802.11p is presented in typical urban and freeway scenarios.

Distributed mobile cloud computing (MCC) is the new paradigm for providing ubiquitous cloud resources to mobile users with low latency. Mobility is an important factor in distributed MCC which may incur intermittent connectivity and consequently fail computation offloading requests. Latest researches on human mobility show that mobility of users present inherent patterns, periodicity, and predictability. This motivates us to propose a mobile access prediction algorithm based on tail matching subsequence, whose effectiveness and accuracy is validated by experiments using reality mobility dataset. Then MAGA, a mobility-aware offloading decision method for distributed MCC is proposed in this paper for single-job, multicomponent, and multisite offloading scenario. The proposed mobile access prediction is used in MAGA for cloudlet reliability estimation. An integer encoding-based adaptive genetic algorithm is used for offloading decision. Experiment results show the performance advantages of MAGA.

Safety messages propagation is the major task for Vehicular Cyber‐Physical Systems in order to improve the safety of roads and passengers. However, reducing traffic and car accidents can only be achieved by disseminating safety messages in a timely manner with high reliability. Although mathematical modeling of the delay of safety messages is extremely beneficial, analyzing the safety messages propagation is considerably complex due to the high dynamics of vehicles. Moreover, most previous works assume vehicles drive independently and the interaction between vehicles is not taken into consideration. In this paper, we proposed an analytical model to describe the performance of safety messages propagation in the VCPSs under platoon‐based driving pattern. Infrastructure‐less and RSU‐supported scenarios are evaluated independently. The analytical model also takes into account different transmission situations and various system parameters, such as communication range, traffic flow, and platoon size. The effectiveness of the analytical model is verified through simulation and the impacts of different parameters on the expected transmission delay are investigated. The results will help determine the system design parameters to satisfy the delay requirement for safety applications in VCPSs.

Ternary Content Addressable Memory (TCAM) is widely used for caching flow entries in Software Defined Network (SDN). However, limited by the current technology level and hardware cost, the capacity of TCAM is difficult to meet the needs for caching flow entries of large scale SDN, which would seriously affect the scalability of SDN. Aimed at the problem, some researches improve the efficiency of TCAM capacity by setting different lifecycle for different flow entries. Such as Openflow protocol proposed the default flow entry lifecycle, AHTM scheme dynamicly adjusted flow entry lifecycle according to the number of interrupted flows. However, these schemes didn't consider the dynamic features of network traffic in SDN, and hardly conform to the actual network demands. In order to effectively utilize TCAM capacity and thus enhance the scalability of SDN, we propose TF-IdleTimeout scheme to dynamically set flow entry lifecycle according to real-time network traffic in SDN-based data center network. Two criterion, called Flow Entry Missing Number and Flow Dropping Number, are used to evaluate the effect. The results show that, compared with existing schemes, TF-IdleTimeout scheme can greatly improve the utilization efficiency of TCAM capacity in SDN.

Diverse applications in vehicular network present specific requirements and challenges on wireless access technology. Although considered as the first standard, IEEE 802.11p shows the obvious drawbacks and is still in the field-trial stage. In this paper, we propose long-term evolution (LTE)-V as a systematic and integrated V2X solution based on time-division LTE (TD-LTE) 4G. LTE-V includes two modes: 1) LTE-V-direct and 2) LTE-V-cell. Comparing to IEEE 802.11p, LTE-V-direct is a new decentralized architecture which modifies TD-LTE physical layer and try to keep commonality as possible to provide short range direct communication, low latency, and high reliability improvements. By leveraging the centralized architecture with native features of TD-LTE, LTE-V-cell optimizes radio resource management for better supporting V2I. LTE-V-direct and LTE-V-cell coordinate with each other to provide an integrated V2X solution. Performance simulations based on sufficient scenarios and the prototype system with typical cases are presented. Finally, future works of LTE-V are envisioned.

The integration cellular networks, wireless local area networks (WLANs), and the new paradigm of mobile ad hoc networks (MANETs) is the trend for next generation mobile networks. And the multi-hop routing mechanism is an open and challenge issue in this area. In this paper, a multi-hop routing mechanism with fuzzy estimation of links for heterogeneous wireless networks (HWNs) is proposed. The mechanism comprises neighbor discover, gateway discover and route discovery, which supports the mobile hosts (MHs) outside of the service area to access BS/AP by multi-hop route. The quality of links is addressed and evaluated by comprehensive fuzzy estimation approach based on analytic hierarchy process (AHP). Furthermore, the route maintenance overhead is also analyzed and discussed. Simulations reveal that the proposed routing mechanism can effectively provide valid route and improve the quality of service and performance in HWNs.