V2X Networks Research Articles

Reliable Routing for V2X Networks: A Joint Perspective of Trust Prediction and Attack Resistance

Reliable Routing for V2X Networks: A Joint Perspective of Trust Prediction and Attack Resistance

The Shared Experience Actor–Critic (SEAC) Approach for Allocating Radio Resources and Mitigating Resource Collisions in 5G-NR-V2X Mode 2 Under Aperiodic Traffic Conditions

5G New Radio (NR)-V2X, standardized by 3GPP Release 16, includes a distributed resource allocation Mode, known as Mode 2, that allows vehicles to autonomously select transmission resources using either sensing-based semi-persistent scheduling (SB-SPS) or dynamic scheduling (DS). In unmanaged 5G-NR-V2X scenarios, SB-SPS loses effectiveness with aperiodic and variable data. DS, while better for aperiodic traffic, faces challenges due to random selection, particularly in high traffic density scenarios, leading to increased collisions. To address these limitations, this study models the Cellular V2X network as a decentralized multi-agent networked Markov decision process (MDP), where each vehicle agent uses the Shared Experience Actor–Critic (SEAC) technique to optimize performance. The superiority of SEAC over SB-SPS and DS is demonstrated through simulations, showing that the SEAC with an N-step approach achieves an average improvement of approximately 18–20% in enhancing reliability, reducing collisions, and improving resource utilization under high vehicular density scenarios with aperiodic traffic patterns.

Optimization of the age of correlated information in V2X networks with edge computing

Vehicle-to-Everything (V2X) communication has the potential to revolutionize the travel experience in intelligent transportation, which has received the great attention recently. However, ensuring the freshness of information from multiple sources is critical for the real-time and reliable communication in vehicular networks, especially for timely updates of service centers. To address this issue, we use a promising metric called Age of Correlated Information (AoCI), which can characterize the freshness of multi-source information. Therefore, we propose a novel model that can dynamically regulate the channel activation matching and edge computing collaboration strategy to minimize AoCI in V2X vehicular networks. Firstly, we describe the system model of a V2X network with edge computing, including definitions and assumptions for freshness of information, edge co-computing, etc. Secondly, we formulate the joint optimization problem as a source-related age minimization (SRAM) problem, which is NP-complete. A heuristic algorithm is proposed to solve it under fast-fading channel. Finally, since traditional graph models cannot capture the changing correlation between nodes in dynamic networks, we use graph convolutional networks(GCN) to extract the features of multi-source correlation. The features extracted by GCN include relevant attributes of the sources and its communication links. The features are provided as input to a double deep Q network (DDQN) for training the model that can adapt to a dynamic network environment. Extensive simulation experiments in different network scenarios validate that our proposed method can effectively and efficiently reduce the average AoCI and the computational resources.

Supporting Differentiated Streaming Services in Heterogeneous Vehicle-to-Everything Networks.

Advancements in assisted driving technologies are expected to enable future passengers to use a wide range of multimedia applications in electric vehicles (EVs). To address the bandwidth demands for high-resolution and immersive videos during peak traffic, this study introduces a bandwidth-management algorithm to support differentiated streaming services in heterogeneous vehicle-to-everything (V2X) networks. By leveraging cellular 6G base stations, along with Cell-Free (CF) Massive Multi-Input Multi-Output (mMIMO) Wi-Fi 7 access points, the algorithm aims to provide a high-coverage, high-speed, and low-interference V2X network environment. Additionally, Li-Fi technology is employed to supply extra bandwidth to vehicles with limited connectivity via V2V communication. Importantly, the study addresses the urgency and prioritization of different applications to ensure the smooth execution of emergency applications and introduces a pre-downloading mechanism specifically for non-real-time applications. Through simulations, the algorithm's effectiveness in meeting EV users' bandwidth needs for various multimedia streaming applications is demonstrated. During peak-bandwidth-demand periods, users experienced an average increase in bandwidth of 47%. Furthermore, bandwidth utilization across the V2X landscape is significantly improved.

Proactive Bandwidth Allocation for V2X Networks With Multi-Attentional Deep Graph Learning

Proactive Bandwidth Allocation for V2X Networks With Multi-Attentional Deep Graph Learning

Secure Performance of RIS-Aided NOMA in Cognitive V2X Networks With Imperfect CSI Over Double Rayleigh Fading

Secure Performance of RIS-Aided NOMA in Cognitive V2X Networks With Imperfect CSI Over Double Rayleigh Fading

Supporting Immersive Video Streaming via V2X Communication

With the rapid advancement of autonomous driving and network technologies, future vehicles will function as network nodes, facilitating information transmission. Concurrently, in-vehicle entertainment systems will undergo substantial enhancements. Beyond traditional broadcasting and video playback, future systems will integrate immersive applications featuring 360-degree views and six degrees of freedom (6DoF) capabilities. As autonomous driving technology matures, vehicle passengers will be able to engage in a broader range of entertainment activities while on the move. However, this evolution in video applications will significantly increase bandwidth demand for vehicular networks, potentially leading to bandwidth shortages in congested traffic areas. This paper presents a method for bandwidth allocation for vehicle video applications within the landscape of vehicle-to-everything (V2X) networks. By utilizing a millimeter-wave (mmWave), terahertz (THz) frequency band, and cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) wireless communication technologies, we provide vehicle passengers with the necessary bandwidth resources. Additionally, we address bandwidth contention issues in congested road segments by incorporating communication methods tailored to the characteristics of vehicular environments. By classifying users and adjusting according to the unique requirements of various multimedia applications, we ensure that real-time applications receive adequate bandwidth. Simulation experiments validate the proposed method’s effectiveness in managing bandwidth allocation for in-vehicle video applications within V2X networks. It increases the available bandwidth during peak hours by 32%, demonstrating its ability to reduce network congestion and ensure smooth playback of various video application types.

Spectrum-Energy-Efficient Mode Selection and Resource Allocation for Heterogeneous V2X Networks: A Federated Multi-Agent Deep Reinforcement Learning Approach

Spectrum-Energy-Efficient Mode Selection and Resource Allocation for Heterogeneous V2X Networks: A Federated Multi-Agent Deep Reinforcement Learning Approach

An Integrated DQN and RF Packet Routing Framework for the V2X Network

With the development of artificial intelligence technology, deep reinforcement learning (DRL) has become a major approach to the design of intelligent vehicle-to-everything (V2X) routing protocols for vehicular ad hoc networks (VANETs). However, if the V2X routing protocol does not consider both real-time traffic conditions and historical vehicle trajectory information, the source vehicle may not transfer its packet to the correct relay vehicles and, finally, to the destination. Thus, this kind of routing protocol fails to guarantee successful packet delivery. Using the greater network flexibility and scalability of the software-defined network (SDN) architecture, this study designs a two-phase integrated DQN and RF Packet Routing Framework (IDRF) that combines the deep Q-learning network (DQN) and random forest (RF) approaches. First, the IDRF offline phase corrects the vehicle’s historical trajectory information using the vehicle trajectory continuity algorithm and trains the DQN model. Then, the IDRF real-time phase judges whether vehicles can meet each other and makes a real-time routing decision to select the most appropriate relay vehicle after adding real-time vehicles to the VANET. In this way, the IDRF can obtain the packet transfer path with the shortest end-to-end delay. Compared to two DQN-based approaches, i.e., TDRL-RP and VRDRT, and traditional VANET routing algorithms, the IDRF exhibits significant performance improvements for both sparse and congested periods during intensive simulations of the historical GPS trajectories of 10,357 taxis within Beijing city. Performance improvements in the average packet delivery ratio, end-to-end delay, and overhead ratio of the IDRF over TDRL-RP and VRDRT under different numbers of pairs and transmission ranges are at least 3.56%, 12.73%, and 5.14% and 6.06%, 11.84%, and 7.08%, respectively.

Time-efficient detection of false position attack in 5G and beyond vehicular networks

5G-V2X-enabled transportation systems rely on seamless cooperation between vehicles, infrastructure, and pedestrians, facilitated by the exchange of real-time position and state information among these entities. Misbehaving vehicles try injecting bogus messages into the network, thereby compromising its reliability and security. This work proposes an efficient Deep Learning (DL)-based Misbehavior Detection System (MDS) that leverages the use of Recurrent Neural Networks (RNNs) to analyze message consistency and detect bogus information in 5G-V2X networks. We highlight the significance of incorporating historical data to analyze consistency. Additionally, we emphasize the importance of evaluating the computational overhead induced by MDSs in V2X networks, given the critical need for low latency communication. We validate our work through both experimental and theoretical studies and compare it to existing works. The obtained results show the effectiveness of our work, achieving an accuracy of 95% in detecting false information injection attacks. Additionally, we guarantee a low required time/computing complexity of O(1), thereby avoiding significant overhead impacting the end-to-end latency when exchanging CAMs.

Trust-Aware V2V Relay-Assisted Content Distribution in Cellular V2X Networks

Trust-Aware V2V Relay-Assisted Content Distribution in Cellular V2X Networks

Task-Efficiency Oriented V2X Communications: Digital Twin Meets Mobile Edge Computing

Digital Twin (DT) has emerged as an enabling technology for the sixth generation (6G) vehicle-to-everything (V2X) communications. However, there are two crucial issues on leveraging DT for 6G V2X communications. First, what kind of DT capabilities can be combined with the 6G V2X networks? Second, how to transform the DT capabilities into the practical V2X network performance gain? Motivated to solve these problems, this article investigates the DT capabilities under a DT and mobile edge computing empowered 6G V2X network architecture. Specifically, three DT capabilities are presented: (1) Strengthening the human-machine interaction via driving behavior analysis; (2) Improving the traffic safety via knowledge-based vehicle fault diagnosis; (3) Analyzing the spatial-temporal traffic characteristics via data aggregation. Furthermore, we investigate two case studies for illustrating how the DT capabilities can be utilized to perform the task-efficiency oriented V2X network scheduling. In the first case study, the driver behavior analysis result is combined with the V2X channel scheduling strategy. In the second case study, a deep reinforcement learning based vehicle merging decision is devised in the DT domain. Then, a coalition based V2X channel scheduling strategy is proposed to help accomplish the vehicle merging decision task. Finally, we evaluate the performance of our proposed task-efficiency oriented V2X channel scheduling schemes and highlight the future research directions.

Performance Analysis for Downlink Transmission in Multiconnectivity Cellular V2X Networks

Performance Analysis for Downlink Transmission in Multiconnectivity Cellular V2X Networks

A Simultaneously Transmit and Receive Antenna Terminal for In-Band Full-Duplex Applications

In this paper, a low-profile co-linearly polarized simultaneously transmit-receive antenna (STRA) terminal is presented for 5G and upcoming technologies. The proposed STRA terminal comprises of an identical pair of spatially rotated half-circular microstrip antennas for transmission (Tx) and reception (Rx). The challenge of self-interference (SI) is addressed by employing passive SI cancellation techniques such as spatial diversity, field confinement, and surface perturbations. This has been accomplished by using fence-strip structure and V-shaped slots in the ground plane. The STRA was designed, fabricated, and tested for full-duplex operation in a sub-6 GHz band lying from 5.73 to 5.88 GHz, providing high interport isolation ranging from 35.5 dB to a maximum of 46.5 dB. The proposed STRA was implemented using Rogers 3003™ substrate. The measurements yield radiation characteristics with high gain of 5.45-6.0 dBi and 5-6.5 dBi for Tx and Rx antennas, respectively. The measured results are in good conformance with the simulated design. This antenna finds its potential usage in V2X and private 5G networks and can be extended for full-duplex MIMO implementations.

Misbehavior detection with spatio-temporal graph neural networks

Graph Neural Networks (GNNs) gained the attention of researchers following advancements in Representational Learning. Unlike classical machine learning (ML) methods, its ability to represent more concepts on its data structure gives GNN a clear head start. On the other hand, Misbehavior Detection (MBD) has become a security solution for authorized vehicles to protect against malicious activities on Vehicles to Everything (V2X) Networks. Although authorities standardize MBD, there are not enough MBD datasets for ML, and existing ones are unsuitable for GNNs. In this study, we address this issue by providing an algorithm to convert classical MBD datasets for GNN. Then, a novel GNN model is proposed to detect misbehaving activity on V2X networks. Obtained results show that the proposed GNN model outperforms existing methods with 99.92% accuracy, 99.9196% recall, 31% better runtime efficiency, and 97.35% Matthews correlation coefficient.

Vehicular Positioning Based on GPS/IMU Data Fusion Aided by V2X Networks

Vehicular Positioning Based on GPS/IMU Data Fusion Aided by V2X Networks

Enhancing road safety through advanced predictive analytics in V2X communication networks

"SafeRouteX," which stands for "Enhancing Road Safety through Advanced Predictive Analytics in V2X Communication Networks," is a novel approach to Vehicle-to-Everything (V2X) networks. Despite advancements in vehicle and road safety technologies, traffic accidents continue to be a significant problem. To increase road safety, SafeRouteX uses cutting-edge machine learning to forecast traffic accidents in V2X networks. The first data is processed by a data ingestion layer, and then the temporal patterns and uncertainties in the traffic data are extracted using LSTM-VAE features. The Elastic Net Layer minimizes dimensionality while retaining predictive power after feature selection optimization. A RandomForest classifier, which performs well on complicated, high-dimensional datasets, is the main predictive model. Combining approaches guarantees a solid strategy for the complexity analysis of traffic data. Using 4-fold cross-validation, the accuracy and generalizability of SafeRouteX were thoroughly evaluated across a range of traffic scenarios. This validation approach demonstrated SafeRouteX's ability to anticipate traffic accidents, highlighting its potential to enhance road safety in smart transportation systems.

Quantifying Cyber Risks: The Impact of DoS Attacks on Vehicle Safety in V2X Networks

Quantifying Cyber Risks: The Impact of DoS Attacks on Vehicle Safety in V2X Networks

Gorilla Troops Optimizer with Deep Learning-based Multi-Criteria Decision Making for Traffic Analysis in V2X Networks

Gorilla Troops Optimizer with Deep Learning-based Multi-Criteria Decision Making for Traffic Analysis in V2X Networks

Rescale-Invariant Federated Reinforcement Learning for Resource Allocation in V2X Networks

Rescale-Invariant Federated Reinforcement Learning for Resource Allocation in V2X Networks