The effectiveness of autonomous vehicles relies on reliable perception capabilities. Despite significant advancements in artificial intelligence and sensor fusion technologies, current single-vehicle perception systems continue to encounter limitations, notably visual occlusions and limited long-range detection capabilities. Collaborative Perception (CP), enabled by Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, has emerged as a promising solution to mitigate these issues and enhance the reliability of autonomous systems. Beyond advancements in communication, the computer vision community is increasingly focusing on improving vehicular perception through collaborative approaches. However, a systematic literature review that thoroughly examines existing work and reduces subjective bias is still lacking. Such a systematic approach helps identify research gaps, recognize common trends across studies, and inform future research directions. In response, this study follows the PRISMA 2020 guidelines and includes 106peer-reviewed articles. These publications are analyzed based on modalities, collaboration schemes, and key perception tasks. Through a comparative analysis, this review illustrates how different methods address practical issues such as pose errors, temporal latency, communication constraints, domain shifts, heterogeneity, and adversarial attacks. Furthermore, it critically examines evaluation methodologies, highlighting a misalignment between current metrics and CP’s fundamental objectives. By delving into all relevant topics in-depth, this review offers valuable insights into challenges, opportunities, and risks, serving as a reference for advancing research in vehicular collaborative perception.

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Recent advances in neural rendering, such as NeRF and Gaussian Splatting, have shown great potential for dynamic scene reconstruction in intelligent vehicles. However, existing methods rely on a single ego-vehicle, suffering from limited field-of-view and occlusions, leading to incomplete reconstructions. While V2X communication may provide additional information from roadside infrastructure or other vehicless, it often degrades reconstruction quality due to sparse overlapping views. In this paper, we propose V2X-Gaussians, the first framework integrating V2X communication into Gaussian Splatting. Specifically, by leveraging deformable Gaussians and an iterative V2X-aware cross-ray densification approach, we enhance infrastructure-aided neural rendering and address view sparsity in multi-agent cooperative scenarios. In addition, to support systematic evaluation, we introduce a standardized benchmark for V2X scene reconstruction. Experiments on real-world data show that our method outperforms state-of-the-art approaches by +2.09 PSNR with only 561.8 KB for periodic V2X data exchange, highlighting the benefits of incorporating roadside infrastructure into neural rendering for intelligent transportation systems. Our code and benchmark are publicly available under an open-source license.41https://github.com/abhishekjagtap1/V2X-Guassians

Collaborative perception in automated vehicles lever-ages the exchange of information between agents, aiming to elevate perception results. Previous camera-based collabo-rative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the en-vironment. However, these approaches fall short in offering a comprehensive 3D environmental prediction. To bridge this gap, we introduce the first method for collaborative 3D semantic occupancy prediction. Particularly, it improves local 3D semantic occupancy predictions by hybrid fusion of (i) semantic and occupancy task features, and (ii) Compressed orthogonal attention features shared between vehi-cles. Additionally, due to the lack of a collaborative perception dataset designed for semantic occupancy prediction, we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation. The experimental findings highlight that: (i) our collaborative semantic occupancy predictions excel above the results from single vehicles by over 30%, and (ii) models anchored on semantic occupancy outpace state-of-the-art collaborative 3D detection techniques in subsequent perception applications, showcasing enhanced accuracy and enriched semantic-awareness in road environments.

Infrastructure-assisted Cooperative Intelligent Transportation Systems (C-ITS) leverage roadside intelligent infrastructure and vehicular network technology to facilitate information exchange among traffic participants, enhancing road safety, efficiency, and sustainability. However, this requires not only the massive deployment of infrastructure, including advanced sensors, communication devices, and computing units at various levels, but also the involvement of various stakeholders in the development of functions and real-road testing. In this paper, we present our test field – First Mile, as an open innovation lab for C-ITS in Ingolstadt, Germany, offering an open environment for world-wide stakeholders to conduct research and testing in C-ITS. In particular, we equip a 3.5 km area with 22 roadside intelligent masts and 89 sensors, achieving dense deployment on public roads. Our design includes a protocol stack tailored for different C-ITS stations and services, conforming to European communication standards. Furthermore, we conduct quantitative analyzes of key performance metrics, such as radio signal quality and End-to-End delay, to assess the efficacy of First Mile in different data processing pipelines. Finally, we delve into the future prospects of large-scale C-ITS deployment, guided by extensive and prolonged measurement studies.

Maneuver coordination is envisioned as a future Vehicle-to-Everything (V2X) communication-enabled application for cooperative driving to improve the safety, comfort, and efficiency of driving. A Maneuver Coordination Service (MCS) is currently being standardized to facilitate negotiation and coordination of cooperative maneuvers for the Connected and Automated Vehicles (CAVs) by exchanging Maneuver Coordination Messages (MCMs). In order to ensure fair use of available communication channel resources and limit the channel load to reduce message collisions, the European Telecommunications Standards Institute (ETSI) has defined a Decentralized Congestion Control (DCC) framework with packet rate control at the access and facilities layer. However, the DCC mechanisms are content agnostic and can have a negative impact on the MC application in congested communication environments. This paper proposes a new method by combining an adaptive DCC mechanism with a no packet rate control method for important MCMs during the negotiation and execution of safety-critical and emergency maneuvers. A simulation environment is used to extensively evaluate the new method and demonstrate its advantages over the standardized approach. A challenging V2X scenario with a high number of vehicles is simulated in congested V2X channels with only MCMs and with a mix of V2X message types. The results demonstrate the significant improvement of the proposed method for packet rate control for the MCS.

Federated learning allows for cooperative training among distributed clients by sharing their locally learned model parameters, such as weights or gradients. However, as model size increases, the communication bandwidth required for deployment in wireless networks becomes a bottleneck. To address this, we propose a residual-based federated learning framework (ResFed) that transmits residuals instead of gradients or weights in networks. By predicting model updates at both clients and the server, residuals are calculated as the difference between updated and predicted models and contain more dense information than weights or gradients. We find that the residuals are less sensitive to an increasing compression ratio than other parameters, and hence use lossy compression techniques on residuals to improve communication efficiency for training in federated settings. With the same compression ratio, ResFed outperforms current methods (weight-or gradient-based federated learning) by over 1.4× on federated datasets, including MNIST, FashionMNIST, SVHN, CIFAR-10, CIFAR-100, FEMNIST, in client-to-server communication, and can also be applied to reduce communication costs for server-to-client communication.

In federated learning, all networked clients contribute to the model training cooperatively. However, with model sizes increasing, even sharing the trained partial models often leads to severe communication bottlenecks in underlying networks, especially when communicated iteratively. In this paper, we introduce a federated learning framework FedD3 requiring only one-shot communication by integrating dataset distillation instances. Instead of sharing model updates in other federated learning approaches, FedD3 allows the connected clients to distill the local datasets independently, and then aggregates those decentralized distilled datasets (e.g. a few unrecognizable images) from networks for model training. Our experimental results show that FedD3 significantly outperforms other federated learning frameworks in terms of needed communication volumes, while it provides the additional benefit to be able to balance the trade-off between accuracy and communication cost, depending on usage scenario or target dataset. For instance, for training an AlexNet model on CIFAR-10 with 10 clients under non-independent and identically distributed (Non-IID) setting, FedD3 can either increase the accuracy by over 71% with a similar communication volume, or save 98% of communication volume, while reaching the same accuracy, compared to other one-shot federated learning approaches.

V2X Collective Perception is the principle of exchanging sensor data among V2X-capable stations, such as vehicles or roadside units, by exchanging lists of perceived objects in the 5.9 GHz frequency band for road safety and traffic efficiency. An object can be anything relevant to traffic safety, e.g., vehicles or pedestrians. The current standardization of Collective Perception in Europe considers filtering objects for transmission based on their locally perceived dynamics and freshness to preserve channel resources. However, two remaining problems of object filtering are: information redundancy and adapting object filtering to the available channel resources. In this paper, we combine redundancy mitigation and congestion control-aware filtering. We evaluate the performance of the resulting object filtering techniques by realizing realistic, large-scale simulations of a mid-size city in Germany. We assess the performance using a scoring metric. The results show better information redundancy control and adjustable channel usage for object filtering.

Sensor data sharing has an immense potential to enhance the perception capabilities of vehicles and to provide better situational awareness. It is being standardized as collective perception by the European Telecommunication Standards Institute (ETSI) as part of Cooperative Intelligent Transport Systems (C-ITS). For the transmission of collective perception messages via sidelink in Cellular-V2X, sensing-based semi-persistent scheduling (SB-SPS) in the unmanaged mode of 5G-NR V2X provides low-latency communication among road traffic participants that are located outside the cellular network coverage. The unpredictability of the collective perception messages in periodicity and size poses certain challenges on the SB-SPS, thereby creating poor utilization of radio resources and high risk of resource collisions. Existing system level simulations study the performance of collective perception from the application perspective without addressing the radio resource allocation at the access layer. This work investigates the challenges of the sidelink resource allocation mechanisms in 5G-NR V2X and assesses the impact of the mechanism on the performance of collective perception by simulation in a realistic urban traffic environment. A practical approach is adopted to formulate mathematical models that can characterize the radio resource utilization and resource collisions arising in such environments and yield the appropriate 5G-NR V2X parameters.

The protection of vulnerable road users (VRUs) by means of V2X communication is increasingly considered for the next generation of cooperative safety systems. Two messaging protocols are being standardized: $(i)$ With collective perception for sensor data sharing: VRUs can be passively perceived by the local sensors in vehicles and roadside units, which disseminate this information in dedicated messages carrying lists with VRU and other objects. (ii) VRUs can actively transmit messages in order to make other road users in their vicinity aware of their presence. This paper carries out a performance evaluation of a scenario with the two main messaging protocols and also their combined deployment, and compares them with two baseline approaches. Simulation results for a representative roundabout scenario with manifold interaction among vehicles and pedestrians indicate that the combination of sensor data sharing and active VRU transmissions performs best: It provides the highest VRU detection rate and the shortest VRU detection latency. This approach keeps the channel busy ratio below a critical threshold, which prevents the data congestion control of the V2X communication system from its activation.

Sensor data sharing in vehicular networks can significantly improve the range and accuracy of environmental perception for connected automated vehicles. Different concepts and schemes for dissemination and fusion of sensor data have been developed. It is common to these schemes that measurement errors of the sensors impair the perception quality and can result in road traffic accidents. Specifically, when the measurement error from the sensors - also referred as measurement noise - is unknown and time varying, the performance of the data fusion process is restricted, which represents a major challenge in the calibration of sensors. In this paper, we consider sensor data sharing and fusion in a vehicular network with both, vehicle-to-infrastructure and vehicle-to-vehicle communication. We propose a method, named Bidirectional Feedback Noise Estimation (BiFNoE), in which an edge server collects and caches sensor measurement data from vehicles. The edge estimates the noise and the targets alternately in double dynamic sliding time windows and enhances the distributed cooperative environment sensing at each vehicle with low communication costs. We evaluate the proposed algorithm and data dissemination strategy in an application scenario by simulation and show that the perception accuracy is on average improved by around 80% with only 12 kbps uplink and 28 kbps downlink bandwidth.

Cooperative Intelligent Transport Systems (C-ITS) play a significant role in improving road traffic safety and efficiency. Primary use cases rely on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. In C-ITS, the safety of vulnerable road users (VRUs) such as pedestrians, mo-torists and other users with reduced mobility is being increasingly considered. A warning system, where VRUs actively send and receive messages instead of being passively monitored as road traffic objects, can play an important role in detecting risky situations and allowing the warning of vehicle drivers. However, in a dense urban traffic scenario with closely moving vehicles and pedestrians, the communication network can face severe resource constraints. Considering Cellular-V2X communication systems, the user equipments (UEs) may have to use the unmanaged mode of the sidelink interface. This paper analyzes the limitations of the existing radio resource allocation mechanisms and proposes adaptive strategies to ensure fairness in the distribution of radio resources between vehicles and VRUs. In addition, this work addresses the question about the situations in which VRU safety, being subject to a resource-constrained Cellular-V2X network, can be ensured.

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Cellular communications with its ubiquitous cellular coverage enable various use cases for long-distance aerial inspection, surveillance and monitoring with drones. For these use cases, the exchange of command & control and sensor data, such as video, require a reliable and low-latency communication link. In this paper, we present results of a measurement campaign of drone communications in an experimental 5G network. The measurement scenarios cover (i) the capacity and quality of the link between the drone and the ground station, (ii) the robustness of drone communications with the isolation provided by network slicing, and (iii) the stability of the link between drone and ground station for long-distance flights. The results indicate that cellular communications in principle meet the reliability and latency of video and command & control data for long-distance flights even when the performance of cellular network is optimized for terrestrial devices. The application of network slicing in future 5G networks has a great potential to protect the drone critical command & control data from undesired wireless interferers.

Introduced with LTE Release 14, Cellular V2X enables device-to-device communication to support road safety and traffic efficiency applications. We present Artery-C, a simulation framework for the performance evaluation of Cellular V2X protocols and V2X applications. Our simulator relies on the simulation framework SimuLTE and substantially extends it by implementing control and user planes, a dedicated sidelink interface with specific focus on dynamic mode switching and some advanced features of 5G mobile networks, such as variable numerologies. Artery-C integrates seamlessly into the simulation framework Artery, which enables the simulation of standardized V2X messages at the facilities layer as well as the coupling to the mobility simulator SUMO. In order to demonstrate the capabilities of Artery-C, we evaluate V2X-based platooning as a representative use case and present results for mode 3, mode 4 and mode switching in a highway scenario.

ITS-G5 is a communication system for vehicle-to-everything communication for road safety and traffic efficiency applications. On the lower layers, it is based on IEEE 802.11 standard and uses a simple ad hoc mode with a random medium access control scheme. Ad hoc networking is realized by a geographical routing scheme that provides single- and multi-hop communication over ITS-G5 links for periodic and event-driven broadcast messages. Specifically, contention-based forwarding (CBF) allows for efficient and reliable multi-hop packet transport by overhearing and timer-based transmission control. To cope with the channel congestion, decentralized congestion control (DCC) adjusts the message rate and ensures that the network load keeps below a predefined threshold of the bandwidth. To enforce a node's message rate, a “ Gatekeeper” above the MAC and beneath CBF is added. Under high network load, this Gatekeeper introduces an additional queuing delay seen by CBF, which can cause its overhearing function to fail. This contribution studies multi-hop forwarding in the context of DCC with the Gatekeeper and LIMERIC as a rate adaptation algorithm. We propose a congestion-enabled forwarding scheme that, in comparison to existing approaches considering DCC and forwarding separately, restores the efficient operation of CBF, and improves the communication performance in terms of reliability and latency for mixed data traffic composed of single- and multi-hop packets with different priorities. The simulations show the performance improvements in a freeway scenario. An analysis assesses the boundary conditions for CBF and the Gatekeeper. Moreover, the analysis corroborates the simulation.

In intelligent transport systems (ITS), vehicular users broadcast messages with ego information such as position, speed, and other. For exchanging ITS messages directly among vehicles via cellular sidelink transmission, the base station assigns dedicated V2V radio resources for users in coverage. To improve spectral efficiency, the same radio resource can be simultaneously scheduled to multiple vehicles within a cell, e.g., based on a minimum vehicle distance. This distance can be determined by location information obtained from ITS messages. In this work, we introduce a framework for location-based scheduling, exposing basic relations among cell spectral efficiency, communication range and reliability. Based on the framework we show that an optimal scheduling distance exists, which maximizes the cell throughput by guaranteeing range and reliability constraints. The optimized distance can then be utilized by the base station scheduler, which benefits from a closed form solution for the outage probability, derived in this work. We validate that the proposed form with its simplifications achieves a similar performance compared with the optimal solution, obtained by Monte Carlo simulations.

In recent years, two candidates for vehicular communications have evolved for the support of road safety and traffic efficiency applications. On the one hand, ad-hoc networks exist based on the ITS-G5/802.11p protocol stack, and on the other hand, there are cellular network infrastructures based on an extended LTE stack, which we refer to as Cellular-based Vehicular Communication Systems (Cellular-VCS). The most important extension of the classical LTE stack is a direct link among vehicles which is also called Device-to-Device (D2D) communication. Both approaches meet the requirements on vehicular communications but show technology-inherent mechanisms that result in different performances. ITS-G5 features a small latency at a small network load whereas Cellular-VCS promises a highly reliable packet transmission. One of the main difference of both approaches lies in the channel access which is random-based for ITS-G5 and centrally scheduled for Cellular-VCS. This contribution studies the performance of the two named technologies in a real-world road traffic scenario through comprehensive simulations. The simulation scenario makes use of real road traffic density measurements for modeling the mobility of the vehicles. Mixed network data traffic of periodically and event-based messages is generated through particular generation rules. The results prove that both technologies work stable at moderate road traffic conditions but have significant differences in the quantified communication parameters.

Future 5G and beyond mobile networks target at services with a high degree of heterogeneity in terms of their communication requirements. To meet these requirements, different PHY numerologies would provide a better performance; still, all services must be served by a single network technology. Generalized Frequency Division Multiplexing (GFDM) is a good candidate for PHY virtualization where the dimensions of the data block can be dynamically configured in time and frequency. Allocating these blocks in a common spectrum every scheduling period leads to a "packing" problem, in which the QoS demands of the data flows need to be acknowledged. In this paper we consider the optimization of the data block allocation as a Knapsack problem. We incorporate the flows' QoS demands by means of utility theory, where utility functions provide a metric of urgency for a flow to be scheduled and the data block to be allocated. For the resulting two-dimensional geometric Knapsack problem we propose a heuristic solution, assess different design options and evaluate the performance in terms of data rate and queuing delay.

This paper presents a framework for pursuing lowlatency communication among V2V networks underlaying V2I networks. To achieve low-latency communication, solely relying on the improvement of the air-interface may not be enough. To cope with the highly dynamic environment of vehicular networks, a time dynamic optimization approach is proposed that improves the latency performance through not only optimization of spectrum resources but also by constraining the network switching rate. To further decrease the complexity of the time dynamic optimization problem, we convert the original problem to a deterministic optimization problem through the Lyapunov Optimization Theory. The proposed algorithm becomes a more suitable scheme for the vehicular network. Analytical results show that the proposed scheme can approach the best tradeoff between the latency performance and the network switching rate. Simulation results are provided to verify the proposed algorithm.

Vehicular communications have a great potential to improve intersection safety and traffic efficiency. Achieving a high application performance is challenging due to the specific propagation conditions caused by buildings and obstacles found at urban intersections. Relying on the state-of-the-art solution for vehicular communication based on IEEE 802.11, we extend contention-based forwarding to distribute data packets via multiple paths and apply joint decoding on erroneous received data packets. We study the gain of cooperative relaying with joint decoding on the performance of collision avoidance applications in an intersection scenario. We could show that with our algorithm the awareness distance and reliability is increased up to 25m and 55 %, respectively, under poor channel conditions.

The convergence of sensor-based vehicle automation and Inter-Vehicle Communication (IVC) will be a key to achieve the full automation of vehicles. In this paper we present a new method for the design and performance evaluation of Cooperative Automated Driving (CAD) systems, based on a bidirectional coupling of vehicle and network simulators (Webots and ns-3). The coupling exploits the comprehensive capabilities of the simulators at a reasonable computational complexity and allows simulating CAD systems with high accuracy. We demonstrate the capabilities of the simulation tool by a case study of convoy driving with automated vehicles using a fully distributed control algorithm and IVC. The study compares CAD-specific metrics (safety distance, headway, speed) for an ideal and a realistic communication channel. The simulation results underline the need of accurate modeling and give valuable insights for the design of CAD systems.

This paper proposes a novel design for linear precoder in underlay Device-to-Device (D2D) communication using cellular network, specifically for a use case with vehicle-to-vehicle communication and platooning. To increase the communication reliability, we develop an optimization algorithm for precoder that takes into account the outage probability when channel state information (CSI) is only partially available at devices and transmission could be in outage. The algorithm aims at maximizing the sum throughput over each transmission link received at one device, while constraining the interference caused by the transmit power from each device to the cellular network. Due to the intractable form of outage probability, an extended Markov Inequality is used to transform the problem into an upper-bound expression. A two-step alternating algorithm is then adapted to solve the multi- variable optimization. The proposed algorithm is compared with other state-of-the-art technologies in the field of vehicular communication for achieving high throughput. Simulation results show that our proposed algorithm outperforms the current techniques and achieves higher average throughput with extremely low outage probability, thus enables reliable vehicle- to-vehicle communication for platooning in a D2D underlay setting.

Decentralized congestion control (DCC) in ITS-G5 based vehicular ad hoc networks ensures that the requirements of safety and traffic efficiency applications are met even under high vehicle density and channel load conditions. In European standardization, a "gatekeeper" on top of the ITS-G5 MAC sub-layer is being considered that controls a node's packet rate as a function of the channel load. This paper studies the performance of the gatekeeper with packet prioritization and an adaptive linear control algorithm. The simulation results indicate that the gatekeeper with priority queuing (PQ) can effectively handle different packet priorities for multi-hop packets. Our gatekeeper-specific enhancements of the forwarding algorithm yield performance improvements in terms of reliability and latency compared to the plain DCC approach. Finally, we discuss the issue of packet starvation caused by the gatekeeper's PQ scheme that affects the performance of lower-priority packets.

The combination of automated driving and Inter-Vehicle Communication (IVC) allows automated vehicles to drive cooperatively, thereby greatly enhancing their safety and traffic efficiency. Convoys are groups of automated vehicles which keep a multi-lane formation with decentralized control supported by IVC. The vehicle control algorithm of convoy vehicles requires up-to-date information about the neighbor vehicle dynamics; fast and efficient convoy communications enable the cooperative maneuvering of the automated vehicles. For this reason, we evaluate IVC in convoys of automated vehicles by defining performance metrics which quantify the reliability, latency and data age of convoy communications. Our results explore the trade-off between the convoy message frequency and the communication performance; whereas a high message frequency results in a higher number of lost messages and delay due to channel congestion, a low message frequency yields a higher data age of the information available to the vehicle controller. As a result, convoy algorithm designers should choose carefully the optimal value for the convoy message frequency as a function of the required communication performance and the convoy size.

To achieve low delay for vehicular communication in cellular networks, the use of direct device-to-device (D2D) communication among vehicles is regarded as a key functional requirement. When D2D users share the spectrum with regular cellular users (D2D underlay), resource allocation schemes assure the coexistence between D2D and cellular users. Due to the high mobility of the vehicles, the resources need to be reallocated, but frequent updates cause high signaling overhead and degrade the delay performance. In this paper, we present a radio resource re-allocation scheme for vehicular D2D users in a platoon scenario. The scheme reduces the re-allocation rate and gives delay guarantees for each vehicle.With the help of Lyapunov optimization, a closed form of the upper delay bound and resource re-allocation rate is also derived. The simulation results show that the proposed scheme can provide an delay upper bound and simultaneously minimizes the resource re-allocation rate.

This paper presents a routing algorithm for wireless multi-hop ad hoc networks, which applies distributed source coding (DSC) and multi-path transport of data packets. DSC enables the relay nodes to efficiently encode the packets and helps the destination node to correctly decode the information from multiple erroneous copies of the same packet received via different paths. While in conventional communication systems packets with bit errors are discarded, in our design the relays are allowed to forward erroneous packets. In order to exploit DSC for ad hoc routing, we extend contention-based geographical forwarding (CBGF) to support multi-path packet transport and enable the joint decoder to recover transmission errors. Under harsh wireless conditions, the approach promises performance gains compared to the case without joint decoding. In order to evaluate the performance of CBGF, we define an evaluation framework that models the erroneous forwarding and joint decoding by an abstraction of the physical transmission. We assess the performance by simulations for a simple scenario with static nodes in terms of packet success ratio, end-to-end delay, and data traffic overhead, and compare the results with the baseline CBGF scheme without DSC-specific extensions and joint decoding.

It is our great pleasure to welcome you to the 3rd IEEE Workshop on Smart Vehicles: Connectivity Technologies and ITS Applications (SmartVehicles'16), which is held in conjunction with the 13th IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM'16). The aim of this workshop is to bring together academics, industry professionals, and application developers to presents recent developments, current research challenges, and future directions in the area of connected and smart transportation.

Vehicular ad hoc networks based on IEEE 802.11 OCB suffer from channel congestion, which will be critical for safety applications when the channel becomes saturated. Therefore, decentralized congestion control (DCC) is needed that keeps the channel load under a pre-defined threshold. In the European system for vehicular communication, a DCC-gatekeeper has been inserted into the protocol stack that reduces the data traffic generated or forwarded by the vehicle. This DCC-gatekeeper affects the functionality of the contention-based forwarding (CBF) and degrades its performance. In this paper we evaluate the performance of CBF under congestion-free and saturated channel conditions for several protocol variants: DCF, EDCA and DCC-gatekeeper. The results show that the standard DCC-gatekeeper hampers the functionality of CBF even for small node densities. We enhance the DCC-gatekeeper-based approach to overcome this issue and evaluate the protocol variants in terms of reliability, latency and channel load in a freeway scenario with realistic trace-based mobility.

Coordinated multi-point (CoMP) is a well-known approach to mitigate interference in cellular systems and, thus, to improve spectral efficiency. On the downside, implementing CoMP comes at a high cost, e.g., regarding backhaul and signal processing. These costs can be reduced when only BSs at the same site cooperate, referred to as intra-site CoMP. However, the capacity gains of intra-site CoMP are also lower, because intra-site clusters cover only a subset of cell edges. In conventional cellular networks, the deployment of 3 non-overlapping sectors is known to be a good compromise between spatial multiplexing and interference. Using intra-site CoMP, earlier simulation-based work has shown that a higher number of sectors that are largely overlapping are beneficial. In this work, we evaluate multi-cell propagation in an urban testbed, where one site is deployed either with 3 non-overlapping or with 6 overlapping sectors. Moreover, we compare measurements from field trials with simulation results for reference.

Cooperation and information exchange will allow autonomous vehicles to increase their sensing range and maneuver coordinately, thereby greatly enhancing their safety and efficiency. The combination of autonomous driving and vehicular communications will enable Cooperative Autonomous Driving Systems (C-ADS) with stringent requirements. We present two new multi-hop forwarding algorithms specially suited to the dissemination of information in C-ADS: (i) Greedy Broadcast Forwarding, aimed to the distribution of packets within a geographical area, and (ii) Greedy Multicast Forwarding, which delivers packets only to the members of a vehicle group. A performance comparison of the proposed schemes with the broadcast forwarding algorithms defined in the ETSI GeoNetworking standard shows that Greedy Broadcast Forwarding achieves the highest reliability, while Greedy Multicast Forwarding yields the lowest traffic overhead in the considered scenario. These results indicate that the proposed forwarding algorithms are promising candidates to enable multi-hop communications in future C-ADS.

Cooperative multi-cell MIMO techniques are well known for their outstanding capability to mitigate inter-cell interference by allowing user data to be jointly processed by several interfering base stations for the improvement of the system performance. This paper proposes an iterative algorithm that designs the precoding matrix for cooperative multi-cell MIMO downlink communications. As the demand for higher throughput and higher system resilience is envisioned for future wireless communication systems, the proposed algorithm takes the potential outage into consideration when channel state information is only partially available at the transmitter side. The aim is to maximize the sum user throughput considering outage with subject to power limitation at each base station. The joint optimization of the precoding matrix and the assigned transmission rate is solved via a 2-step alternating algorithm. The performance of the proposed method is evaluated by Monte Carlo simulations and compared with existing methods. Simulation results show that our proposed algorithm achieves performance gain than other referenced methods over the entire compared SNR region. Performance of all methods are also compared and analyzed when inter-cluster interference is present.

This paper explores using the first subsymbol in the structure of a GFDM symbol as a pseudo circular preamble. As data and training sequence overlap in the GFDM structure, an initial estimation approach for isolating the preamble information from the data is presented. The concept allows for adaptation of state-of-the-art techniques developed for OFDM in order to estimate time offset at every GFDM symbol. The paper studies the proposal in the context of vehicular communication scenarios and assesses the performance of single-shot estimation of time offset. Both, line-of-sight and non-line-of-sight scenarios with doubly-dispersive wireless channels are considered and compared with the CP-based method from the IEEE 802.11p standard.

The GeoNetworking protocol provides single-hop and multi-hop communication in vehicular ad hoc networks based on IEEE 802.11p/ITS-G5. It has been standardized by the ETSI Technical Committee ITS as part of its Release 1 set of specifications and is expected to be deployed in the next years. This paper presents a performance evaluation of the GeoNetworking protocol in its recently published version. Our study assesses the performance of the broadcast forwarding algorithms for multi-hop packet transport that are used to disseminate information in geographical areas for road safety and traffic efficiency applications. From the algorithms specification in the standard, we derive six variants with different combinations of protocol mechanisms with increasing complexity and assess their performance in terms of reliability, latency, and overhead. The algorithms are evaluated in a reference freeway scenario with bidirectional road traffic and a realistic trace-based mobility model with varying vehicle density. The obtained results indicate that the combination of contention-based and greedy forwarding shows the best overall performance; further functional improvements have a limited performance gain in the studied scenario.

Cellular systems of the fourth generation (4G) have been optimized to provide high data rates and reliable coverage to mobile users. Cellular systems of the next generation will face more diverse application requirements: the demand for higher data rates exceeds 4G capabilities; battery-driven communication sensors need ultra-low power consumption; and control applications require very short response times. We envision a unified physical layer waveform, referred to as generalized frequency division multiplexing (GFDM), to address these requirements. In this paper, we analyze the main characteristics of the proposed waveform and highlight relevant features. After introducing the principles of GFDM, this paper contributes to the following areas: 1) the means for engineering the waveform's spectral properties; 2) analytical analysis of symbol error performance over different channel models; 3) concepts for MIMO-GFDM to achieve diversity; 4) preamble-based synchronization that preserves the excellent spectral properties of the waveform; 5) bit error rate performance for channel coded GFDM transmission using iterative receivers; 6) relevant application scenarios and suitable GFDM parameterizations; and 7) GFDM proof-of-concept and implementation aspects of the prototype using hardware platforms available today. In summary, the flexible nature of GFDM makes this waveform a suitable candidate for future 5G networks.

Beacon and event-driven warning messages are two important messages that are used by many Intelligent Transportation Systems (ITS) applications for improving road safety and traffic efficiency. Beacon messages are regularly broadcast by each vehicle to notify other vehicles of its presence and its status. Event-driven warning messages are emitted when a vehicle or the road infrastructure discerns a critical situation. When congestion occurs, it is necessary to reduce the beacon load to keep a certain amount of the available bandwidth for the event-driven messages because they carry time- critical information of high importance. This paper considers three beacon congestion control algorithms that control the beacon load below a certain threshold by adjusting the transmit rate and/or transmit power for the beacon messages. We perform a large set of simulations to evaluate these algorithms. Our simulation results reveal promising results for these algorithms.

Vehicular communication (VC) systems have the potential to improve road safety and driving comfort. Nevertheless, securing the operation is a prerequisite for deployment. So far, the security of VC applications has mostly drawn the attention of research efforts, while comprehensive solutions to protect the network operation have not been developed. In this paper, we address this problem: we provide a scheme that secures geographic position-based routing, which has been widely accepted as the appropriate one for VC. Moreover, we focus on the scheme currently chosen and evaluated in theCar2CarCommunicationConsortium(C2C-CC). We integrate security mechanisms to protect the position-based routing functionality and services (beaconing, multi-hop forwarding, and geo-location discovery), and enhance the network robustness. We propose defense mechanisms, relying both on cryptographic primitives and plausibility checks mitigating false position injection. Our implementation and initial measurements show that the security overhead is low and the proposed scheme deployable.

Despite great advantages of vehicular ad hoc networks (VANETs), they also introduce challenges with respect to security and privacy. Today, people are more and more concerned about their privacy. Using unique identifiers for communication, a vehicle can easily be located and tracked. Alternatively, a technical solution to protect drivers' privacy is the use of changing pseudonyms. Existing work mainly focuses on algorithms for pseudonym change and neglect practical implications and realizability. For deployment and integration of pseudonymity into a VANET communication system, several issues need to be solved. This paper analyzes the practical challenges and proposes protocol- and implementation-related solutions necessary to turn pseudonymity support into practice. Finally, the paper concludes by means of analysis and measurements that the burden of pseudonymity can be alleviated at reasonable costs and compromises in anonymity support.

Abstract To solve the IP mobility problem, the use of multicast has been proposed in a number of different approaches, applying multicast in different characteristic ways. We provide a systematic discussion of fundamental options for multicast‐based mobility support and the definition and experimental performance evaluation of selected schemes. The discussion is based on an analysis of the architectural, performance‐related, and functional requirements. By using these requirements and selecting options regarding network architecture and multicast protocols, we identify promising combinations and derive four case studies for multicast‐based mobility in IP‐based cellular networks. These case studies include both the standard any‐source IP multicast model as well as non‐standard multicast models, which optimally utilize the underlying multicast. We describe network architecture and protocols as well as a flexible software environment that allows to easily implement these and other classes of mobility‐supporting multicast protocols. Multicast schemes enable a high degree of flexibility for mobility mechanisms in order to meet the service quality required by the applications with minimal protocol overhead. We evaluate this overhead using our software environment by implementing prototypes and quantifying handoff‐specific metrics, namely, handoff and paging latency, packet loss and duplication rates, as well as TCP goodput. The measurement results show that these multicast‐based schemes improve handoff performance for high mobility in comparison to the reference cases: basic and hierarchical Mobile IP. Comparing the multicast‐schemes among each other the performance for the evaluated metrics is very similar. As a result of the conceptual framework classification and our performance evaluations, we justify specific protocol mechanisms that utilize specific features of the multicast. Based on this justification, we advocate the usage of a source‐specific multicast service model for multicast‐based mobility support that adverts the weaknesses of the classical Internet any‐source multicast service model. Copyright © 2006 John Wiley & Sons, Ltd.

Vehicular ad hoc networks (VANETs) enable new applications by providing self-organizing vehicle-to-vehicle and vehicle-to-roadside communication. Some of these applications require reliable, in-order data delivery across end-to-end connections. The performance of a vehicular transport protocol (VTP) that provides such a service depends on its ability to adapt quickly to the varying path characteristics of highly dynamic environments. This paper studies path characteristics of VANETs in highway scenarios. An analytical evaluation derives upper bounds on the expected connectivity and disruption duration. Simulations validate these results and study further metrics, such as packet loss, packet reordering and round trip times. The paper also briefly outlines a preliminary VTP design that incorporates these analytical results.