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This chapter treats two important topics related to vehicle‐to‐everything (V2X): spectrum and channel modeling. It gives an overview of the spectrum used for V2X communications along with the regulations and recommendations suggested by the different regulatory agencies to achieve spectrum harmonization among the regions and technologies. The chapter focuses mainly on European countries and their regulations, but it also includes detailed information about other relevant areas such as the United States and Asia. It covers the relevant state‐of‐the‐art channel models for V2X communication, along with describing recent V2V channel measurements and models. The chapter describes the state‐of‐the‐art channel models, including their most relevant components: line‐of‐sight blockage analysis, path‐loss and shadow‐fading modeling, and fast‐fading modeling. Finally, it concludes by analyzing the compliance of recent channel measurements with existing standards and providing possible recommendations for further enhancements.

Wireless vehicular communications and sensing technologies are key to enabling more advanced intelligent transportation systems (ITSs) with improved safety and efficiency. Within the realm of wireless communication, millimeter-wave (mmwave) technology has recently received much attention, providing rich spectrum resources to support the timely transmission of large amounts of data. This is especially important for vehicular applications because the number of sensors on modern vehicles is rapidly increasing and thus generating large amounts of data. To fully exploit this potential, understanding mm-wave vehicle-to-vehicle (V2V) propagation channels is crucial. In this article, we review the state of the art in mm-wave V2V channel measurements and modeling, describe recent directional V2V channel measurements performed in the 60-GHz band, and discuss future challenges to be addressed in mm-wave V2V channel measurements and modeling.

Terahertz (THz) spectrum band has garnered a lot of interest recently, due to the advances in supporting radio electronics and the existence of hundreds of Gigahertz of available spectrum. Therefore, it is considered as one of the key enablers for the upcoming sixth generation (6G) wireless networks, which target to provide Terabit per second communication services. However, the propagation loss due to the high carrier frequency, obstacles, and the atmospheric attenuation in this frequency band need to be compensated to enable usable coverage ranges. In this paper, we investigate the potential of using Intelligent Reflecting Surface (IRS) for enhancing the coverage of Terahertz communication. We conduct a case study for an indoor cellular communication scenario with multiple Transmission and Reception Points supported by IRS. The numerical result show that the IRS can enable significant coverage extension. Besides, by optimizing the frequency resource allocation and IRS selection jointly considering the frequency-dependent atmospheric absorption, the throughput of underserved users can be improved significantly.

Advanced wireless communication use cases relying on sidelinks require guaranteed network performance. However, fulfilling the desired Quality-of-Service (QoS) requirements can-not be always ensured due to factors such as varying interference and dynamicity in the network. To reduce the impact of varying network performance on sidelink applications, we address the problem of long-term proactive adaptation of sidelink applications based on network performance. Specifically, we propose a modular algorithmic framework comprising of data-driven prediction models and model-based optimization to predict the feasibility of given application data rate requirements. Further-more, the framework is capable of predicting the simultaneously achievable data rate demands (in the max-min sense). A vehicular communication scenario is considered as an example to show the applicability of the framework. Evaluations show that variations in feasibility and achievable data rate demands can be well captured to enable robust application layer adaptations.

Vehicle-to-vehicle (V2V) channels exhibit unique properties due to the highly dynamic environment and low elevation of the antennas at both ends of the link. Of particular importance for the behavior of V2V channels, and consequent reliability of the communication link, is the severity and dynamics of blockage of both the line-of-sight and other multipath components (MPCs). The characteristics of blockage become more important as the carrier frequency increases, and the ability of the signal to penetrate through objects diminishes. To characterize the effects of vehicle blockage, we performed V2V channel measurements in four different frequency bands (6.75, 30, 60, and 73 GHz) in urban and highway scenarios. We analyzed the impact of the blocker size and position on the received power and fast fading parameters, as well as the frequency dependence of these parameters under blockage. Our results show that there is a strong influence of the size of the blocking vehicle on the blockage loss and the angular/delay spread. The position of the blocker relative to the transmitter and receiver also plays an important role. On the other hand, the frequency dependence is quite limited, with the blockage loss increasing slightly and the number of scattered MPCs reducing slightly as frequency increases. The main conclusion of this paper is that V2V communication will be possible in high (millimeter-wave) frequencies, even in the case of blockage by other vehicles.

Cellular networks enable high reliability of vehicle-to-vehicle (V2V) communications thanks to centralized, efficient coordination of radio resources. Collision-free transmissions are possible, where base stations could allocate orthogonal resources to the vehicles. However, in case of limited resources in relation to the data traffic load, the resource allocation task becomes a challenge. Current solutions propose heuristic algorithms that focus on resource reuse, often based on the location of the vehicles. Such schedulers are mainly designed assuming ideal network coverage conditions and are prone to performance degradation in case of coverage loss. Further, they typically rely on frequent scheduling updates, which increases the dependency on coverage. In this paper, we propose a reinforcement learning-based approach to scheduling V2V communications. Our solution, called iVRLS, delivers higher reliability than an enhanced version of a state-of-the-art benchmark algorithm in case of intermittent coverage conditions, while requiring less frequent scheduling. Following this approach, we enable a unified scheduler deployment irrespective of coverage, which offers graceful performance behavior across varying coverage conditions, thus making iVRLS a robust alternative to existing schedulers.

The Third Generation Partnership Project (3GPP) has recently published its Release 16 that includes the first Vehicle to-Everything (V2X) standard based on the 5G New Radio (NR) air interface. 5G NR V2X introduces advanced functionalities on top of the 5G NR air interface to support connected and automated driving use cases with stringent requirements. This paper presents an in-depth tutorial of the 3GPP Release 16 5G NR V2X standard for V2X communications, with a particular focus on the sidelink, since it is the most significant part of 5G NR V2X. The main part of the paper is an in-depth treatment of the key aspects of 5G NR V2X: the physical layer, the resource allocation, the quality of service management, the enhancements introduced to the Uu interface and the mobility management for V2N (Vehicle to Network) communications, as well as the co-existence mechanisms between 5G NR V2X and LTE V2X. We also review the use cases, the system architecture, and describe the evaluation methodology and simulation assumptions for 5G NR V2X. Finally, we provide an outlook on possible 5G NR V2X enhancements, including those identified within Release 17., Comment: This tutorial paper has been accepted for publication as open access in the IEEE Communications Surveys & Tutorials journal. Citation information: DOI 10.1109/COMST.2021.3057017

Future 5G systems have set a goal to support mission-critical Vehicle-to-Everything (V2X) communications and they contribute to an important step towards connected and automated driving. To achieve this goal, the communication technologies should be designed based on a solid understanding of the new V2X applications and the related requirements and challenges. In this regard, we provide a description of the main V2X application categories and their representative use cases selected based on an analysis of the future needs of cooperative and automated driving. We also present a methodology on how to derive the network related requirements from the automotive specific requirements. The methodology can be used to analyze the key requirements of both existing and future V2X use cases.

The ultimate goal of next-generation vehicle-toeverything (V2X) communication systems is enabling accident-free, cooperative automated driving that uses the available roadway efficiently. To achieve this goal, the communication system will need to enable a diverse set of use cases, each with a specific set of requirements. We discuss the main usecase categories, analyze their requirements, and compare them against the capabilities of currently available communication technologies. Based on the analysis, we identify a gap and indicate possible system designs for the fifth-generation (5G) V2X that could close the gap. Furthermore, we discuss an architecture of the 5G V2X radio access network (RAN) that incorporates diverse communication technologies, including current and cellular systems in centimeter wave (cm-wave) and millimeter wave (mm-wave), IEEE Standard 802.11p [1], and vehicular visible light communications (VVLC). Finally, we discuss the role of future 5G V2X systems in enabling more efficient vehicular transportation: from improved traffic flow and reduced intervehicle spacing on highways to coordinated intersections in cities (the cheapest way to increasing the road capacity) to automated smart parking (no more visits to the parking garage!), all of which will ultimately enable seamless end-to-end personal mobility.

To meet the Quality-of-Service (QoS) requirements of vehicular applications, some knowledge of future wireless channel statistics is essential. We address the problem of predicting channel quality between vehicles in terms of path loss which, exhibits strong fluctuations over time due to highly dynamic vehicular environment. We propose a framework for data-driven path loss prediction models that are obtained from datasets comprising information related to message transmissions and the communication scenario. By combining changepoint detection method and online learning, the proposed framework adapts the current prediction model based on its performance, thus accounting for the dynamics in the environment and the cost of re-training. Evaluations using real world Vehicle-to-Vehicle communications datasets show that adapting the prediction function using the proposed framework can achieve prediction accuracy comparable to that of online learning case, while significantly reducing the number of data samples required for re-training.

Vehicle-to-vehicle (V2V) communications performance depends significantly on the approach taken to schedule the radio resources. When the infrastructure is available, so far the best performing V2V scheduling algorithms are based on centralized approach. In case there is no infrastructure, sensing the resources in a distributed manner to determine whether a specific resource is free performs well. We propose a hybrid solution, where a centralized reinforcement learning (RL) algorithm provides a candidate subset of resources, whereas a distributed sensing mechanism, running on each vehicle, makes the final resource selection. We evaluate the performance of the proposed approach in an out-of-coverage setting and show that it outperforms the state-of-the-art algorithms in highly dynamic scenarios by using the best of both worlds: RL agent provides optimized long-term resource allocations, while the distributed sensing handles temporary and unforeseen network conditions that can not be predicted effectively.

We introduce simultaneous multi-band ultra wideband double-directional measurements at 6.75 GHz, 30 GHz, and 60 GHz in a corner scenario at a "T" intersection in an urban environment. We analyse the influence of the dynamics from LOS to NLOS on the large-scale parameters, showing that the propagation channel offers similar scattering opportunities at the sub-6 GHz and the mm-wave bands. In addition, we discuss the possibility of utilizing the directional information from sub-6 GHz to steer beams in the mm-waves bands.

Vehicle-to-vehicle (V2V) communications have distinct challenges that need to be taken into account when scheduling the radio resources. Although centralized schedulers (e.g., located on base stations) could be utilized to deliver high scheduling performance, they cannot be employed in case of coverage gaps. To address the issue of reliable scheduling of V2V transmissions out of coverage, we propose Vehicular Reinforcement Learning Scheduler (VRLS), a centralized scheduler that predictively assigns the resources for V2V communication while the vehicle is still in cellular network coverage. VRLS is a unified reinforcement learning (RL) solution, wherein the learning agent, the state representation, and the reward provided to the agent are applicable to different vehicular environments of interest (in terms of vehicular density, resource configuration, and wireless channel conditions). Such a unified solution eliminates the necessity of redesigning the RL components for a different environment, and facilitates transfer learning from one to another similar environment. We evaluate the performance of VRLS and show its ability to avoid collisions and half-duplex errors, and to reuse the resources better than the state of the art scheduling algorithms. We also show that pre-trained VRLS agent can adapt to different V2V environments with limited retraining, thus enabling real-world deployment in different scenarios., Article accepted to IEEE CAVS 2019

Predicting the performance of vehicular communication networks is challenging due to the interplay of multiple factors. One prominently influencing factor is the wireless channel between the transmitter and the receiver. We address the problem of predicting the path loss between two communicating vehicles by using a non-parameterized, data-driven approach. Specifically, we apply Random Forest, a non-parametric learning method, to real world vehicle-to-vehicle communications dataset and evaluate it with respect to its prediction accuracy and generalization capability. We show that availability of additional information to the non-parametric model results in better performance than the well known parameterized log distance path loss model. We further discuss the relative contribution of different features for the model accuracy and conclude that careful selection of features can achieve results nearly as accurate as using all available features. Finally, we discuss several aspects that need to be considered while using such data-driven prediction models along with applications of V2V path loss prediction.

Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled either by a centralized scheduler residing in the network (e.g., a base station in case of cellular systems) or a distributed scheduler, where the resources are autonomously selected by the vehicles. The former approach yields a considerably higher resource utilization in case the network coverage is uninterrupted. However, in case of intermittent or out-of-coverage, due to not having input from centralized scheduler, vehicles need to revert to distributed scheduling. Motivated by recent advances in reinforcement learning (RL), we investigate whether a centralized learning scheduler can be taught to efficiently pre-assign the resources to vehicles for out-of-coverage V2V communication. Specifically, we use the actor-critic RL algorithm to train the centralized scheduler to provide non-interfering resources to vehicles before they enter the out-of-coverage area. Our initial results show that a RL-based scheduler can achieve performance as good as or better than the state-of-art distributed scheduler, often outperforming it. Furthermore, the learning process completes within a reasonable time (ranging from a few hundred to a few thousand epochs), thus making the RL-based scheduler a promising solution for V2V communications with intermittent network coverage., Article published in IEEE VNC 2018

We perform simultaneous vehicle-to-vehicle (V2V) multi-band double-directional measurements at 6.75 GHz, 30 GHz, and 60 GHz in a street canyon environment. We analyse both line-of-sight channels and those obstructed by vehicle blocker. For the different bands, we analyse delay spread, angular spread, vehicle blockage loss, and path loss. Finally, we discuss how the system aspects influence the radio channel.

Reliable and accurate multipath clustering results are essential to design and parameterize geometry based (stochastic) channel models proposed by organizations such as 3GPP, COST, WINNER and others. Clustering within wireless channel models assumes that multipath components a rrive/depart with similar properties in the considered parameter domains. We propose a new approach combining tracking and initialization of the classic K-Means clustering algorithm. The results show a significantly improved consistency for cluster estimation, which subsequently allows for analyzing the lifetime of these clusters. Finally, we apply the proposed approach on two urban macro channel sounding datasets and compare them to the current 3GPP standard. We found that the angular parameters are rather similar to the standard whereas the cluster delay spreads and the number of rays per cluster differ significantly.

This paper investigates the behaviour of Vehicleto-Infrastructure (V2I) channels when propagation conditions transition from Line-of-Sight (LoS) to Non-Line-of-Sight (NLoS) and when the vehicle is approaching an open square from a narrow street. The measurement data used is obtained from a channel sounding campaign at 2.53 GHz center frequency in the city of Bonn, Germany. The channel is investigated in terms of average receive power separated into coand crosspolar components. Furthermore, second order statistics such as delay and angular spreads are analysed. We observe a similar behaviour when links transition between LoS and NLoS and between open square and narrow street. In both cases power decreases and spreads increase. A transition area can be identified that starts/ends well before/after the actual LoS area or open square indicating the necessity of proper modelling of such areas.

In the present paper we introduce novel simultaneous multi-band double-directional ultra-wideband measurements at 6.75 GHz, 30 GHz, and 60 GHz in an urban V2V scenario. The objective is to characterize propagation differences between the measured bands in view of correlated multi-band channel models. Furthermore, we investigate the impact of other vehicles blocking the V2V link by performing measurements with different blocker sizes (specifically, a passenger car and a large van). The shadowing effects and the correlation between intra-cluster delay spreads and large scale parameters are investigated in the different bands.

Due to the dynamic nature of vehicular traffic and the road surroundings, vehicle-to-vehicle (V2V) propagation characteristics vary greatly on both small- and large-scale. Recent measurements have shown that both large static objects (e.g., buildings and foliage) as well as mobile objects (surrounding vehicles) have a profound impact on V2V communication. At the same time, system-level Vehicular Ad Hoc Network (VANET) simulators by and large employ simple statistical propagation models, which do not account for surrounding objects explicitly. We designed GEMV$^2$ (Geometry-based Efficient propagation Model for V2V communication), which uses outlines of vehicles, buildings, and foliage to distinguish the following three types of links: line of sight (LOS), non-LOS due to vehicles, and non- LOS due to static objects. For each link, GEMV$^2$ calculates the large-scale signal variations deterministically, whereas the small- scale signal variations are calculated stochastically based on the number and size of surrounding objects. We implement GEMV$^2$ in MATLAB and show that it scales well by using it to simulate radio propagation for city-wide networks with tens of thousands of vehicles on commodity hardware. We make the source code of GEMV$^2$ freely available. Finally, we validate GEMV$^2$ against extensive measurements performed in urban, suburban, highway, and open space environment., Preprint of an article that will be published in IEEE Transactions on Vehicular Technology

Recent research has shown that both radio and visible light waves can be used to enable communications in highly dynamic vehicular environments. However, the roles of these two technologies and how they interact with each other in future vehicular communication systems remain unclear. Understanding the propagation characteristics is an essential step in investigating the benefits and shortcomings of each technology. To this end, we discuss salient properties of radio and visible light propagation channels, including radiation pattern, path loss modeling, noise and interference, and channel time variation. Comparison of these properties provides an important insight that the two communication channels can complement each other’s capabilities in terms of coverage and reliability, thus better satisfying the diverse requirements of future cooperative intelligent transportation systems.

Next generation 5G communication systems will need to employ a range of diverse technologies in order to support vehicle-to-everything (V2X) use cases, ultimately leading to accident-free, cooperative autonomous vehicles that use the available roadway efficiently. V2X is considered as one of the most challenging applications of 5G, as it requires ultra-reliable and low-latency communication for safety-critical use cases and has to provide high data rates in many scenarios. This paper discusses design aspects for the radio access in 5G V2X. Selected key technologies and their integration towards future 5G V2X physical layer are addressed. We discuss channel modeling in the context of 5G V2X use cases and we present first results for frame structure and numerology design, coexistence with earlier systems, multi-link synchronization, and multi-antenna transmission techniques.

We investigate the evolution of line of sight (LOS) blockage over both time and space for vehicle-to-vehicle (V2V) channels. Using realistic vehicular mobility and building and foliage locations from maps, we first perform LOS blockage analysis to extract LOS probabilities in real cities and on highways for varying vehicular densities. Next, to model the time evolution of LOS blockage for V2V links, we employ a three-state discrete-time Markov chain comprised of the following states: i) LOS; ii) non-LOS due to static objects (e.g., buildings, trees, etc.); and iii) non-LOS due to mobile objects (vehicles). We obtain state transition probabilities based on the evolution of LOS blockage. Finally, we perform curve fitting and obtain a set of distance-dependent equations for both LOS and transition probabilities. These equations can be used to generate time-evolved V2V channel realizations for representative urban and highway environments. Our results can be used to perform highly efficient and accurate simulations without the need to employ complex geometry-based models for link evolution., Comment: 7 pages, 7 figures, accepted in 2016 IEEE 84th Vehicular Technology Conference (VTC2016-Fall)

Due to the differences in terms of antenna height, scatterer density, and relative speed, V2I links exhibit different propagation characteristics compared to V2V links. We develop a geometry-based path loss and shadow fading model for V2I links. We separately model the following types of V2I links: line-of-sight, non-line-of-sight due to vehicles, non-line-of-sight due to foliage, and non-line-of-sight due to buildings. We validate the proposed model using V2I field measurements. We implement the model in the GEMV2 simulator, and make the source code publicly available.

A thorough understanding of the communications channel between vehicles is essential for realistic modeling of Vehicular Ad Hoc Networks (VANETs) and the development of related technology and applications. The impact of vehicles as obstacles on vehicle-to-vehicle (V2V) communication has been largely neglected in VANET research, especially in simulations. Useful models accounting for vehicles as obstacles must satisfy a number of requirements, most notably accurate positioning, realistic mobility patterns, realistic propagation characteristics, and manageable complexity. We present a model that satisfies all of these requirements. Vehicles are modeled as physical obstacles affecting the V2V communication. The proposed model accounts for vehicles as three-dimensional obstacles and takes into account their impact on the LOS obstruction, received signal power, and the packet reception rate. We utilize two real world highway datasets collected via stereoscopic aerial photography to test our proposed model, and we confirm the importance of modeling the effects of obstructing vehicles through experimental measurements. Our results show considerable obstruction of LOS due to vehicles. By obstructing the LOS, vehicles induce significant attenuation and packet loss. The algorithm behind the proposed model allows for computationally efficient implementation in VANET simulators. It is also shown that by modeling the vehicles as obstacles, significant realism can be added to existing simulators with clear implications on the design of upper layer protocols.