A Multi-Layer Collaboration Framework for Industrial Parks with 5G Vehicle-to-Everything Networks

The fifth-generation(5G)wireless communication networks are expected to play an essential role in the transformation of vertical industries.Among many exciting applications to be enabled by 5G,logistics tasks in industry parks can be performed more efficiently via vehicle-to-everything(V2X)communications.In this paper,a multi-layer collaboration framework enabled by V2X is proposed for logistics management in industrial parks.The proposed framework includes three layers:a perception and execution layer,a logistics layer,and a configuration layer.In addition to the collaboration among these three layers,this study addresses the collaboration among devices,edge servers,and cloud services.For effective logistics in industrial parks,task collaboration is achieved through four functions:environmental perception and map construction,task allocation,path planning,and vehicle movement.To dynamically coordinate these functions,device–edge–cloud collaboration,which is supported by 5G slices and V2X communication technology,is applied.Then,the analytical target cascading method is adopted to configure and evaluate the collaboration schemes of industrial parks.Finally,a logistics analytical case study in industrial parks is employed to demonstrate the feasibility of the proposed collaboration framework.

FSD与V2X的优劣对比

特斯拉将准备FSD引入中国,距离自动驾驶又近了一步。自动驾驶的FSD与车联网的V2X自动驾驶相比,谁更有优势?该文从原理、人工驾驶、系统的角度,对FSD与V2X的优劣做了对比。从实现的角度看,FSD更容易实现;从成本的角度看,FSD成本更低;从人工驾驶角度看,FSD缺少了声音传感器;从定位角度看,FSD无法在整车偏移或偏转时准确定位;从系统角度看,FSD是单机操作,容易实现,但效能无法与车联网的V2X自动驾驶匹敌;V2X涉及基站系统、需要全网元统一接口,投资巨大,互联互通困难,建设周期长,尚无商业网实践经验。PSD与V2X各有所长,PSD适用于智能网联的初级阶段,V2X适用于智能网联的中、后期阶段。

Distributed robust power control in two-tier vehicle networks under uncertain channel environments

This paper proposes a novel optimization scheme to support stable and reliable vehicle-to-everything connections in two-tier networks,where the uplink channel of the cellular user is reused by underlay vehicle-to-vehicle communications.However,considering complex channel fading and high-speed vehicle movement,the cer-tainty assumption is impractical and fails to maintain power control strategy in reality in the traditional statical vehicular networks.Rather than the perfect channel state information assumption,the first-order Gauss-Markov process which is a probabilistic model affected by vehicle speed and fading is introduced to describe imperfect channel gains.Moreover,interference management is a major challenge in reusing communications,especially in uncertain channel environments.Power control is an effective way to realize interference management,and optimal power allocation can ensure that interference of the user meets the communication requirements.In this study,the sum-rate-oriented power control scheme and minimum-rate-oriented power control scheme were implemented to manage interference and satisfy different design objectives.Since both of these schemes are non-convex and intractable,the Bernstein approximation and successive convex approximation methods were adopted to transform the original problems into convex ones.Furthermore,a novel distributed robust power control al-gorithm was developed to determine the optimal solutions.The performance of the algorithm was evaluated through numerical simulations,and the results indicate that the proposed algorithm is effective in vehicular communication networks with uncertain channel environments.

Intelligent Network Slicing in V2X Networks-A Comprehensive Review

The rise of the Internet of Things and autonomous systems has made connecting vehicles more critical.Connected autonomous vehicles can create diverse communication networks that can improve the environment and offer contemporary applications.With the advent of Fifth Generation(5G)networks,vehicle-to-everything(V2X)networks are expected to be highly intelligent,reside on superfast,reliable,and low-latency connections.Network slicing,machine learning(ML),and deep learning(DL)are related to network automation and optimization in V2X communication.ML/DL with network slicing aims to optimize the performance,reliability of the V2X networks,personalized services,costs,and scalability,and thus,it enhances the overall driving experience.These advantages can ultimately lead to a safer and more efficient transportation system.However,existing long-term evolution systems and enabling 5G technologies cannot meet such dynamic requirements without adding higher complexity levels.ML algorithms mitigate complexity levels,which can be highly instrumental in such vehicular communication systems.This study aims to review V2X slicing based on a proposed taxonomy that describes the enablers of slicing,a different configuration of slicing,the requirements of slicing,and the ML algorithm used to control and manage to slice.This study also reviews various research works established in network slicing through ML algorithms to enable V2X communication use cases,focusing on V2X network slicing and considering efficient control and management.The enabler technologies are considered in light of the network requirements,particular configurations,and the underlying methods and algorithms,with a review of some critical challenges and possible solutions available.The paper concludes with a future roadmap by discussing some open research issues and future directions.

Machine learning in vehicular networking:An overview

As vehicle complexity and road congestion increase,combined with the emergence of electric vehicles,the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential.The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks.Particularly,5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities.Machine Learning(ML),a powerful methodology for adaptive and predictive system development,has emerged in both vehicular and conventional wireless networks.Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions,such as traditional control loop design and optimization techniques.This article provides a short survey of ML applications in vehicular networks from the networking aspect.Research topics covered in this article include network control containing handover management and routing decision making,resource management,and energy efficiency in vehicular networks.The findings of this paper suggest more attention should be paid to network forming/deforming decision making.ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies,such as mobile edge computing for real-world deployment.Research datasets,simulation environment standardization,and method interpretability also require more research attention.

A polarisation coding scheme based on an integrated sensing and communication system

Integrated sensing and communication(ISAC)technology enhances the spectrum utilization of the system by interchanging the spectrum between communication and sensing,which has gained popularity in scenarios such as vehicle-to-everything(V2X).With the aim of providing more dependable services for vehicles in high-speed mobile scenarios,we propose a scheme based on sense-assisted polarisation coding.Specifically,the base station acquires the vehicle's positional information and channel strength parameters through the forward time slot echo information.This information informs the creation of the coding architecture for the following time slot.This approach not only optimizes resource consumption but also enhances system dependability.Our simulation results confirm that the introduced scheme displays a notable improvement in the bit error rate(BER)when compared to traditional communication frameworks,maintaining this advantage across both unimpeded and compromised channel conditions.

Modeling and Simulation of Packet Delivery Rate in LTE-V Network Based on Markov Chain

As one of the most promising communication technologies for vehicular networks, LTE-V has the advantages of wide coverage and a high transmission rate. 3 GPP released the technical specification of LTE-V in March 2017, launching a spate of related research and industrialization. In this paper, we propose a communication model based on Markov process to evaluate the reliability of LTE-V. We derived the Packet Delivery Rate(PDR) of LTE-V based on the model. Moreover, we use Poisson process to model the distribution of vehicles on a highway,then combine the communication model with the vehicles’ distribution to derive the PDR under this scenario. To verify the correctness of the proposed model, we established a simulation program on the MATLAB platform. By comparing the simulation results and the mathematical results, we found that simulation results are a very good fit for the model.

Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

In this paper,we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles(HEVs)on a road with slope.Moreover,it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything(V2X),including geographic information,vehicle-to-infrastructure(V2I)information and vehicle-to-vehicle(V2V)information.The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator,where the running route is generated based on real-world data with slope and intersection position.The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time.To show the HEV powertrain characteristics,a case study is given with the speed planning and energy management strategy.

On-board torque management approach to the E-COSM benchmark problem with a prediction-based engine assignment

This paper proposes an energy management strategy for the benchmark problem of E-COSM 2021 to improve the energy efficiency of hybrid electric vehicles(HEVs)on a road with a slope.We assume that HEVs are in a connected environment with real-time vehicle-to-everything information,including geographic information,vehicle-to-infrastructure information and vehicle-to-vehicle information.The benchmark problem to be solved is based on HEV powertrain control using traffic information to achieve fuel economy improvements while satisfying the constraints of driving safety and travel time.The proposed strategy includes multiple rules and model predictive control(MPC).The rules of this strategy are designed based on external environment information to maintain safe driving and to determine the driving mode.To improve fuel economy,the optimal energy management strategy is primarily considered,and to perform real-time energy management via RHC-based optimization in a connected environment with safety constraints,a key issue is to predict the dynamics of the preceding vehicle during the targeted horizon.Therefore,this paper presents a real-time model-based optimization strategy with learning-based prediction of the vehicle’s future speed.To validate the proposed optimization strategy,a powertrain control simulation platform in a traffic-in-the-loop environment is constructed,and case study results performed on the constructed platform are reported and discussed.