V2V Communication System Research Articles

Machine-Learning-Based Path Loss Prediction for Vehicle-to-Vehicle Communication in Highway Environments

Vehicle-to-vehicle (V2V) communication, which plays an important role in intelligent transportation systems, has been statistically proven to improve traffic efficiency and reduce the probability of accidents. In real-world applications, it is critical to accurately estimate the path loss parameter in communication channels due to the variable and complex propagation environments often encountered in inter-vehicle communication scenarios. This paper presents a study on various machine learning methods to improve path loss estimation in V2V communication using a dataset (192,000 observations) obtained from field measurements of highway environments in the Trabzon and Gümüşhane provinces in Türkiye. For this purpose, path loss estimation was carried out with different machine learning algorithms such as Artificial Neural Networks, Random Forest, Linear Regression, Gradient Boosting, Support Vector Regression, and AdaBoost by using various environmental and system features. Then, performance comparisons were conducted between machine learning methods and traditional empirical approaches such as log-distance, two-ray, and log-ray. Examining the outputs reveals that machine learning methods outperform traditional methods and yield results quickly. As a result, the Random Forest and Gradient Boosting methods demonstrated the highest prediction performances, with R2 values of 0.97 and 0.96, MAE values of 0.0557 and 0.0701, and RMSE values of 0.0774 and 0.0964, respectively, outperforming both empirical methods, other machine learning techniques, and the existing studies based on V2V. Overall, our study provides significant contributions to the existing literature by providing a comprehensive parameter set for highway environments, examining the path loss prediction performance of machine learning models with different capabilities, and comparing them with traditional methods. This study not only fills a critical gap in the existing literature but also highlights the necessity, efficiency, and originality of machine learning approaches for improving reliable V2V communication systems.

Artificial neural network-based sparse channel estimation for V2V communication systems

Abstract Artificial neural networks (ANNs) have gained a lot of attention from researchers in the past few years and have been employed on a large scale. They have also been gaining momentum in wireless communication systems. For efficient vehicle-to-vehicle (V2V) channel communication, a sparse multipath channel issue must be studied. To minimize the multipath effect, a time reversal (TR) operation and time division synchronization orthogonal frequency division multiplexing (TDS-OFDM) have been appealing because of their fast synchronization and active spectral efficiency. To improve the transceiver's execution in a frequency-selective fading channel environment, an OFDM system is used to reduce inter- symbol interference (ISI). Simultaneous Orthogonal Matching Pursuit (SOMP) channel state estimator algorithm suffer from high computational cost and high computational complexity. The ANN algorithm has better performance than SOMP algorithm. The proposed neural network technologies have lower complexity than the SOMP algorithm. The application of ANN is capable of solving complex problems, such as those encountered in image, signal processing and have been implemented for channel estimation in OFDM. The proposed ANN outperformed the SOMP algorithm with regard to signal compensation. Overall, the ANN algorithm achieved the best performance. This study proposes an ANN-based sparse channel state estimator. Regarding the bit error rate (BER) metric, the proposed estimator outperforms the channel estimation approach based on the SOMP. The simulation results confirm the efficacy of the proposed approach.

Road to Efficiency: V2V Enabled Intelligent Transportation System

Intelligent Transportation Systems (ITSs) have grown rapidly to accommodate the increasing need for safer, more efficient, and environmentally friendly transportation options. These systems cover a wide range of applications, from transportation control and management to self-driving vehicles to improve mobility while tackling urbanization concerns. This research looks closely at the important infrastructure parts of vehicle-to-vehicle (V2V) communication systems. It focuses on the different types of communication architectures that are out there, including decentralized mesh networks, cloud-integrated hubs, edge computing-based architectures, blockchain-enabled networks, hybrid cellular networks, ad-hoc networks, and AI-driven dynamic networks. This review aims to critically analyze and compare the key components of these architectures with their contributions and limitations. Finally, it outlines open research challenges and future technological advancements, encouraging the development of robust and interconnected V2V communication systems in ITSs.

Li-Fi Technology for Vehicle-To-Vehicle

The integration of Light Fidelity (Li-Fi) technology within Smart Cities to enhance vehicle-to-vehicle (V2V) communication systems. Highlighting the limitations of traditional radio frequency (RF) methods in managing increasing traffic and potential ecosystem impacts, the abstract introduces Li-Fi as a promising alternative. Li-Fi utilizes visible light to achieve high-speed data transfer rates, offering advantages such as enhanced efficiency, reduced latency, improved reliability, and heightened security. The objectives of implementing Li-Fi in V2V communication are delineated, emphasizing its potential to facilitate real-time data exchange crucial for autonomous driving and safety applications. The abstract further details the methodology and architecture of Li-Fi technology, illustrating its utilization of LED bulbs and photo detectors for data transmission and reception. Practical implementation strategies for integrating Li-Fi into V2V communication systems, including sensor-based distance measurement and automated collision avoidance mechanisms, are discussed. Ultimately, the abstract concludes by emphasizing Li-Fi's potential as a cost-effective solution to mitigate traffic accidents and enhance communication efficiency in Smart Cities. Keywords — Li-Fi, Illumination, Wi-Fi, LED, Photo Diode, Radio Frequency

V2V Communication in Smart Traffic Systems: Current status, challenges and future perspectives

Implementing V2V communication in intelligent traffic systems has been a topic of growing interest due to its potential to improve road safety and traffic flow. However, the widespread adoption of V2V communication in intelligent traffic systems could be enhanced by various challenges, such as infrastructure cost, security, and interoperability. These challenges must be addressed to improve the potential benefits of V2V communication. The problem statement of this study is to examine the potential benefits and challenges of V2V communication in intelligent traffic systems and provide insights into the current state of the art in V2V communication research. The proposed approach is to conduct a literature review of recent research on V2V communication in intelligent traffic systems to identify the potential benefits and challenges of V2V communication and the current state of the art in V2V communication research. The study aims to contribute to the field by comprehensively understanding the implementation of V2V communication in intelligent traffic systems and informing potential future research in this area. V2V communication can improve road safety and traffic flow in intelligent traffic systems. However, more research is needed to address the challenges and develop advanced and cost-effective V2V communication systems.

Vehicle to Vehicle Communication Using Li-Fi Technology

Abstract: Vehicle-to-vehicle (V2V) communication has emerged as a pivotal technology in the automotive industry, enabling enhanced safety, efficiency, and convenience on the roads. This abstract explores the integration of Light Fidelity (LiFi) technology in V2V communication systems. LiFi, utilizing visible light communication (VLC), offers high-speed wireless connectivity by using light-emitting diodes (LEDs) to transmit data. By harnessing LiFi's unique advantages such as increased bandwidth, low latency, and immunity to radio frequency interference, V2V communication systems can facilitate real-time exchange of critical information between vehicles, including traffic conditions, collision warnings, and cooperative driving maneuvers. This paper delves into the technical aspects, challenges, and potential applications of LiFi-enabled V2V communication, shedding light on its promising role in revolutionizing the future of smart and connected transportation systems

Improve performance and energy efficiency of plug-in fuel cell vehicles using connected cars with V2V communication

The proliferation of intelligent systems within the transportation sector has presented a favorable prospect for expediting the advancement of novel eco-friendly modes of transportation. Fuel cell vehicles offer a notable environmental benefit through reduced emissions. When combined with vehicle-to-vehicle (V2V) communication, a key driver in the realm of autonomous vehicles, they demonstrate potential for energy conservation. This study aims to investigate the performance of such vehicles. The performance of fuel cells (FCs) has been examined in relation to driving conditions. Research has demonstrated that as speed increases, the proportion of FC usage also increases. Additionally, plug-in vehicles exhibit a similar trend, contingent upon the utilization of an appropriate algorithm, in accordance with the driving conditions. The efficacy of navigation is compromised and the consumption of battery power is diminished. The study demonstrated that reduced stopping and acceleration instances during driving cycles result in decreased energy consumption for vehicles. Additionally, the V2V communication system revealed that traffic control parameters, specifically the safety time (ts) between two cars, significantly affect vehicle performance and fuel consumption. In the FTP-72 cycle, an increase in ts led to an increase in the car's travel range and a decrease in battery usage. Conversely, in other driving cycles, an increase in ts resulted in decreased travel range and increased energy consumption.

Enhancing Security in Vehicle-to-Vehicle Communication: A Comprehensive Review of Protocols and Techniques

Vehicle-to-vehicle (V2V) communication has played a pivotal role in modern intelligent transportation systems, enabling seamless information exchange among vehicles to enhance road safety, traffic efficiency, and overall driving experience. However, the secure transmission of sensitive data between vehicles remains a critical concern due to potential security threats and vulnerabilities. This research focused on investigating the security protocols that have been employed in vehicle-to-vehicle communication systems. A comprehensive review and analysis of relevant literature and research papers was conducted to gather information on existing V2V communication security protocols and techniques. The analysis encompassed key areas, including authentication mechanisms, encryption algorithms, key management protocols, and intrusion detection systems specifically applicable to V2V communication networks. Within the context of real-world V2V environments, this study delved into the challenges and limitations associated with implementing these protocols. The research aimed to provide a comprehensive understanding of the strengths and weaknesses of the current V2V communication security protocols. Furthermore, based on the findings, this paper proposes improvements and recommendations to enhance the security measures of the V2V communication protocol. Ultimately, this research contributes to the development of more secure and reliable V2V communication systems, propelling the advancement of intelligent transportation technology.

Design of Flexible and Dual Band Antenna for Vehicular Communication

The antenna with bowtie and meander lines shaped elements for the vehicular communication in this paper. The dimensions of the proposed antenna are 40.5*91.5*0.1. The substrate used in this antenna is polyamide substrate with a permittivity of 4.3. The implemented antenna is operated at cellular (975MHz) and C-V2X (5.9GHz) band. The efficiency of this proposed antenna is 99.5%. This paper describes the design of antenna with high efficiency vehicular roof mounted for V2V communication system used for ubiquitous intelligent systems. To enhance the connectivity among vehicles by providing seamless communication and to reduce initial access time using high performance antenna systems is the purpose of the ubiquitous intelligent systems. V2V communication efficiency depends on the antenna efficiency used for ubiquitous intelligent systems. We are proposing this antenna to increase the performance of the antenna by enhancing efficiency.

Coalitional Game Based Resource Allocation in D2D-Enabled V2V Communication

The joint resource block (RB) allocation and power optimization problem is studied to maximize the sum-rate of the vehicle-to-vehicle (V2V) links in the device-to-device (D2D)-enabled V2V communication system, where one feasible cellular user (FCU) can share its RB with multiple V2V pairs. The problem is first formulated as a nonconvex mixed-integer nonlinear programming (MINLP) problem with constraint of the maximum interference power in the FCU links. Using the game theory, two coalition formation algorithms are proposed to accomplish V2V link partitioning and FCU selection, where the transferable utility functions are introduced to minimize the interference among the V2V links and the FCU links for the optimal RB allocation. The successive convex approximation (SCA) is used to transform the original problem into a convex one and the Lagrangian dual method is further applied to obtain the optimal transmit power of the V2V links. Finally, numerical results demonstrate the efficiency of the proposed resource allocation algorithm in terms of the system sum-rate.

Linear/Non-Linear Energy Harvesting Models via Multi-Antenna Relay Cooperation in V2V Communications

Vehicle-to-vehicle (V2V) communications is a part of next-generation wireless networks to create smart cities with the connectivity of intelligent transportation systems. Besides, green communications is considered in V2V communication systems for energy sustainability and carbon neutrality. In this scope, radio-frequency (RF) energy harvesting (EH) provides a battery-free energy source as a solution for the future of V2V communications. Herein, the employment of RF-EH in V2V communications is considered where the bit error probability (BEP) of a dual-hop decode-and-forward relaying system is obtained depending on the utilization of antennas at the relay. The multiple antenna power-constraint relay harvests its power by applying dedicated antenna (DA)/power splitting (PS) EH modes and linear (L)/nonlinear (NL) EH models. Moreover, the links between nodes are exposed to double-Rayleigh fading. Finally, the performance of different system parameters is compared using theoretical derivations of BEP. The results provide a comprehensive analysis of the proposed system considering PS/DA-EH modes and L/NL-EH models, as well as deterministic/uniformly distributed placement of nodes. It is observed that PS-EH outperforms DA-EH assuming a placement of an equal number of antennas and distances. Moreover, optimal performance of PS/DA-EH is achieved by allocating more power and increasing the number of antennas for EH, respectively.

ALAMOUTI SPACE-TIME CODING FOR VEHICULAR COMMUNICATIONS IN THE PRESENCE OF CHANNEL ESTIMATION ERRORS

In this paper, the error performance of the Alamouti space-time coding (STC) scheme is investigated for vehicle-to-vehicle (V2V) communication systems over imperfect cascaded fading channels. In vehicular communication systems, perfect knowledge of the channel state information is not available to the users at all times due to the rapid movement of communicating vehicles and fast change of the rich scattering environment which makes the fading effects in wireless channels more severe. Therefore, in the analysis, we consider the erroneous estimation of the channel gain, which is more realistic for practical scenarios. For this purpose, we first derive the moment-generation function (MGF) of the channel fading coefficient with estimation error in the case of the cascaded Nakagami-m fading conditions. Then, using the MGF, we obtain the closed-form symbol-error-rate (SER) expressions of Alamouti STC with two transmitting and L receiving antennas for the M-PSK and M-QAM modulation schemes. Then, the exact ergodic capacity expression is derived for the proposed system. Furthermore, the analytical results are verified through Monte-Carlo simulations. Numerical results show that the SER performance of V2V communication systems can be improved significantly by using Alamouti STC even in case of harsh fading conditions and full channel-state information is not available due to estimation errors.

Channel modeling for V2V communication: Enhancing road safety and intelligent transportatio

The advancement of the Internet of Things (IoT) and Vehicle-to-Vehicle (V2V) communication networks has led to increased research efforts toward developing efficient and innovative V2V communication systems. These systems are designed to achieve the goal of improving road safety and enabling intelligent transportation. A critical aspect of this effort is channel modeling. In this context, this paper summarizes the characteristics of V2V communication channels. It categorizes them into three main types: Deterministic models, Non-geometry stochastic models (NGSMs), and Geometry-based stochastic models (GBSMs). It then reviews recent literature on these three types of channel modeling methods and discusses future development directions and challenges for V2V channel modeling. The study highlights the importance of accurate channel modeling for successful V2V communication systems and emphasizes the need to consider the unique features of V2V channels. It also identifies the limitations of existing channel models and suggests areas of research that can lead to more reliable and efficient V2V communication systems.

Maximal Ratio Combining Detection in OFDM Systems with Virtual Carriers Over V2V Channels.

This paper examines the performance of orthogonal frequency division multiplexing (OFDM) systems for vehicle-to-vehicle (V2V) communication channels. More specifically, a doubly selective channel under high intercarrier interference (ICI) is considered. Current solutions involve complex detection and/or reduced spectral efficiency receivers. This paper proposes the use of virtual carriers (VC) in an OFDM system with a low-complexity maximal ratio combining (MRC) detector to improve the bit error rate (BER) performance. The results show that VC provides diversity in received data, resulting in a ≥5 dB gain compared to previous OFDM systems with conventional linear/nonlinear detectors used as a reference. The detector presented in this paper has linear complexity, making it a suitable solution for real-time V2V communication systems.

Deep Reinforcement Learning-Based Adaptive Beam Tracking and Resource Allocation in 6G Vehicular Networks with Switched Beam Antennas

In this paper, we propose a novel switched beam antenna system model integrated with deep reinforcement learning (DRL) for 6G vehicle-to-vehicle (V2V) communications. The proposed system model aims to address the challenges of highly dynamic V2V environments, including rapid changes in channel conditions, interference, and Doppler effects. By leveraging the beam-switching capabilities of switched beam antennas and the intelligent decision making of DRL, the proposed approach enhances the performance of 6G V2V communications in terms of throughput, latency, reliability, and spectral efficiency. The proposed work develops a comprehensive mathematical model that accounts for 6G channel modeling, beam-switching, and beam-alignment errors. The Proposed DRL framework is designed to learn optimal beam-switching decisions in real time, adapting to the complex and varying V2V communication scenarios. The integration of the proposed antenna system and DRL model results in a robust solution that is capable of maintaining reliable communication links in a highly dynamic environment. To validate the proposed approach, extensive simulations were conducted and performance analysis using various performance metrics, such as throughput, latency, reliability, energy efficiency, resource utilization, and network scalability, was analyzed. Results demonstrate that the proposed system model significantly outperforms conventional V2V communication systems and other state-of-the-art techniques. Furthermore, the proposed approach shows that the beam-switching capabilities of the switched beam antenna system and the intelligent decision making of the DRL model are essential in addressing the challenges of 6G V2V communications.

A Secure Blockchain Enabled V2V Communication System Using Smart Contracts

In recent years, the corporate and industrial sectors have been experiencing significant transformations in vehicle-to-vehicle (V2V) communication. It can improve vehicle safety by giving signals to other vehicles wirelessly. The latest software, hardware, and technologies are applied to develop trusted applications that make V2V communication more believable. Today, various technologies are incorporated into vehicles to remove the barrier to existing challenges. The connected vehicles in V2V communication use sensors, data storage, and communication devices. Vehicles can communicate using the latest secure and trusted Cellular Vehicle-to-Everything (C-V2X) technology using direct and network communication modes. Even connected vehicles suffer from data security, user privacy, reliable environment, and vehicle security. Blockchain can help to eliminate those issues in V2V communication systems. Herein, a secure blockchain-enabled V2V communication system (BVCS) is proposed to enhance the security of vehicles and secure data sharing and communication among vehicles. The developed smart contracts in this paper can authenticate users and their vehicles automatically. In this paper, the proposed algorithms can authenticate users, detect unauthorized access, and establish secure communication between vehicles. The proposed system can enhance data security, user privacy, and vehicle security and provide a trusted environment in V2V communication systems.

NOMA-enabled Cooperative V2V Communications with Fixed-Gain AF Relaying

By virtue of its improving bandwidth efficiency along with user fairness, non-orthogonal multiple access (NOMA) technique is considered a promising method for next-generation wireless communication systems. Since fading effect of wireless channels in vehicle-to-vehicle (V2V) communication systems are more severe than those in traditional systems, in this study, we employ the power-domain downlink NOMA technique in cooperative V2V communication systems to enhance data transmission capacity and network efficiency. In the proposed system, the base station communicates with two vehicular nodes, namely near and far users, through a relay vehicle employing the fixed-gain amplify-and-forward scheme. In real-life scenarios, the relay and the users move in high velocities; hence, the corresponding fading channels between these nodes are exposed as having double-Rayleigh fading characteristic in which the fading coefficient of a wireless channel is modeled as the product of two Rayleigh distributed random variables. To analyze the system performance, we first investigate the outage probability and derive its exact closed-form expressions for the near and far users. Then, we make the exact ergodic capacity analysis and obtain the closed-form solution for the near user. Furthermore, outage and ergodic performances of the NOMA-enabled system are compared to the simulation results of the traditional orthogonal multiple access approach. We also give analytic and numerical results to evaluate the performance of the proposed system and show the consistency of Monte-Carlo simulations with analytical derivations. It is observed that even with the small power allocation, both performances of the near user mostly outperform the far user.

Physical layer security performance of NOMA-assisted vehicular communication systems over double-Rayleigh fading channels

In this paper, we investigate the secrecy performance of a downlink non-orthogonal multiple access enabled V2V communication system wherein a source vehicle communicates with two authenticated user vehicles, i.e., far user and near user, in the presence of a passive eavesdropper vehicle. Moreover, we formulate two scenarios based on the eavesdropper’s decoding capabilities; (1) Scenario I: when the eavesdropper vehicle has comparable decoding capabilities as with the authorized user vehicles, and (2) Scenario II: when the eavesdropper is entirely capable of perfectly decoding the signals from both authorized user vehicles. For such a system configuration with Scenarios I &II, we deduce the analytical expressions for the secrecy outage probability (SOP) and ergodic secrecy capacity over independent but not necessarily identically distributed double-Rayleigh fading channels. Further, to obtain insights into the secrecy diversity order for the legitimate user vehicles under Scenarios I &II, we present the asymptotic SOP analysis by taking three cases into account; (1) Case 1: when the average transmit signal-to-noise ratio approaches infinity, (2) Case 2: when the average channel gains of the user vehicles tend to infinity with fixed average channel gains corresponding to the eavesdropper, and (3) Case 3: when the average channel gains pertaining to the user vehicles and the eavesdropper tend to infinity. From which, we can infer that the secrecy diversity order of the far user vehicle is zero for Cases 1, 2, & 3, whereas the secrecy diversity order of the near user vehicle is zero for Cases 1 & 3 and one for Case 2, under Scenarios I &II. The numerical and simulation results corroborate our theoretical investigations. Our results demonstrate the impact of transmit power, power allocation factor, channel conditions of legitimate users and eavesdropper on the system’s secrecy performance.

Power Allocation and Performance Analysis in Overlay Cognitive Cooperative V2V Communication System With Outdated CSI

In this paper, an active-user cooperative scheme for overlay cognitive radio (OCR) vehicle-to-vehicle (V2V) communication system based on three-dimensional (3D) channel model is proposed. Based on the proposed cooperative scheme, the achievable rate regions of the primary users (PU) and secondary users (SU) with outdated channel state information (CSI) are analyzed. According to the tradeoff between the achievable rates of PU and SU, three power allocation schemes are proposed using outdated CSI. The performance of PU and SU in terms of outage events are analyzed. Based on the analytical framework, the simulation results for achievable rate and outage probabilities are provided. And the impact of power allocation coefficient of PU and SU, outdated CSI, signal-to-noise ratio(SNR) and the codes cross-correlation on the proposed active-user cooperation is analyzed. The simulation results are compared with validated analysis to confirm the theoretical analysis.

A Geometry-Based Stochastic Model for Truck Communication Channels in Freeway Scenarios

Vehicle-to-vehicle (V2V) wireless communication systems are fundamental in many intelligent transportation applications, e.g., traffic load control, driverless vehicle, and collision avoidance. Hence, developing appropriate V2V communication systems and standardization require realistic V2V propagation channel models. However, most existing V2V channel modeling studies focus on car-to-car channels; only a few investigate truck-to-car (T2C) or truck-to-truck (T2T) channels. In this paper, a hybrid geometry-based stochastic model (GBSM) is proposed for T2X (T2C or T2T) channels in freeway environments. Next, we parameterize this GBSM from the extensive channel measurements. We extract the multipath components (MPCs) by using a joint maximum likelihood estimation (RiMAX) and then determine the cluster types based on their evolution patterns. We classify the determined clusters into line-of-sight, single-bounce reflections from static interaction objects (IOs), single-bounce reflections from mobile IOs, multiple-bounce reflections, and density multipath components (DMCs). Particularly, we model multiple-bounce reflections as double clusters following the COST 273/COST2100 method. This paper presents the complete parameterization of the channel model. We validate this model by comparing the delay spread and the angular spreads of arrival/departure obtained from the proposed model with the measurement data.