V2V Transmission Research Articles

A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks

Efficient spectrum sharing is essential for maximizing data communication performance in Vehicular Networks (VNs). In this article, we propose a novel hybrid framework that leverages Multi-Agent Reinforcement Learning (MARL), thereby combining both centralized and decentralized learning approaches. This framework addresses scenarios where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. We introduce the QMIX technique with the Deep Q Networks (DQNs) algorithm to facilitate collaborative learning and efficient spectrum management. The DQN technique uses a neural network to approximate the Q value function in high-dimensional state spaces, thus mapping input states to (action, Q value) tables that facilitate self-learning across diverse scenarios. Similarly, the QMIX is a value-based technique for multi-agent environments. In the proposed model, each V2V agent having its own DQN observes the environment, receives observation, and obtains a common reward. The QMIX network receives Q values from all agents considering individual benefits and collective objectives. This mechanism leads to collective learning while V2V agents dynamically adapt to real-time conditions, thus improving VNs performance. Our research finding highlights the potential of hybrid MARL models for dynamic spectrum sharing in VNs and paves the way for advanced cooperative learning strategies in vehicular communication environments. Furthermore, we conducted an in-depth exploration of the simulation environment and performance evaluation criteria, thus concluding in a comprehensive comparative analysis with cutting-edge solutions in the field. Simulation results show that the proposed framework efficiently performs against the benchmark architecture in terms of V2V transmission probability and V2I peak data transfer.

Multi-agent deep reinforcement learning based resource management in heterogeneous V2X networks

In Heterogeneous Vehicle-to-Everything Networks (HVNs), multiple users such as vehicles and handheld devices and infrastructure can communicate with each other to obtain more advanced services. However, the increasing number of entities accessing HVNs presents a huge technical challenge to allocate the limited wireless resources. Traditional model-driven resource allocation approaches are no longer applicable because of rich data and the interference problem of multiple communication modes reusing resources in HVNs. In this paper, we investigate a wireless resource allocation scheme including power control and spectrum allocation based on the resource block reuse strategy. To meet the high capacity of cellular users and the high reliability of Vehicle-to-Vehicle (V2V) user pairs, we propose a data-driven Multi-Agent Deep Reinforcement Learning (MADRL) resource allocation scheme for the HVN. Simulation results demonstrate that compared to existing algorithms, the proposed MADRL-based scheme achieves a high sum capacity and probability of successful V2V transmission, while providing close-to-limit performance.

Fractional mayfly optimization algorithm‐based Infrastructure‐to‐Vehicle and Vehicle‐to‐Vehicle scheduling for service message transmission in IoV‐fog

SummaryFor the rising number of vehicles, and the advancement of communication and computation technologies, the perception of Internet of Vehicles (IoV) is introduced. It is utilized to capture vehicle‐based information that includes road description, road congestion, vehicle speed, and location. However, this information is important, and it showed more benefits in different ways, like route selection and message dissemination. IoV is the self‐structured network composed with vehicles lies in the road and the road side units (RSUs). It offers Infrastructure‐to‐Vehicle (I2V), as well as Vehicle‐to‐Vehicle (V2V) data transmission mechanism for transmitting service messages. To reliably broadcast the service information to the intended recipient in the IoV network still faced issues. Hence, an efficient service message transmission protocol is developed using the proposed fractional mayfly optimization algorithm (FMA) for selecting the relay vehicle and cooperative vehicle for transmitting service messages to the destination vehicle from RSU through the process of I2V and V2V scheduling. The RSU selects the relay vehicle for every service message using the proposed algorithm and allocates the cooperative vehicle by RSU for scheduling V2V transmission. The simulation results showed that the proposed scheduling method obtains the best channel quality indicator (CQI), delay, distance, packet delivery ratio (PDR), and throughput value of 0.92 for 150 vehicles and 0.005891, 6.060731, 83.45%, and 171.50 Mbps for 100 vehicles.

CAVVPM: Challenge-Based Authentication and Verification of Vehicle Platooning at Motorway.

As a result of vehicle platooning, advantages including decreased traffic congestion and improved fuel economy are expected. Vehicles in a platoon move in a single line, closely spaced, and at a constant speed. Vehicle-to-vehicle communications and sensor data help keep the platoon formation in place, and the CACC system is responsible for maintaining it. In reality, V2V transmissions are essential for reducing platooning distances while still ensuring their safety and security. It is far more difficult to confirm the veracity of a V2V message’s content than it is to verify its integrity and source authentication. Only platoon members can send and receive V2V communications by implementing a practical access control mechanism. The goal is to link a prospective platoon member’s digital identification to their actual location inside the unit. A physical challenge–response interaction is used in the CAVVPM process to verify that a prospective platoon member respects the rules. The applicant is asked to perform a series of random longitudinal movements, thus, the protocol’s name. Remote attackers cannot join the platoon or send bogus CACC communications because CAVVPM blocks them. CAVVPM is more resistant to pre-recording assaults than previous work, and it can validate that the candidate is precisely behind the verifier in the same lane compared to previous studies.

Novel Optical Scattering-Based V2V Communications With Experimental Analysis

Taking advantage of the proliferation of optical wireless communications, this paper proposes a new paradigm of optical scattering (or ultraviolet) vehicle-to-vehicle (V2V) communication with experimental analysis based on extensive channel measurements at a wavelength of 266 nm. This new optical scattering-based V2V technology can ease a fundamental requirement of pointing, acquisition, and tracking (PAT) commonly found in conventional optical V2V systems and can also provide an additional robust capability of efficient non-line-of-sight (NLOS) V2V transmission. Based on the experiments, it is demonstrated that in a mobile V2V channel where the dynamics are changing rapidly, the scattering of the UV photons in the upper atmosphere produces some unique characteristics, resulting in greater randomness and severe fading. From the experimental results, we characterize the channel by a non-stationary time-frequency-selective fading channel with a statistical description of the time-frequency-varying power profile. An examination of the measured data reveals that the distribution of the fading statistics follows the Rayleigh distribution but a departure from the Rayleigh distribution is also observed. In addition, to facilitate the real-time capture of the dynamic behavior of the UV-based V2V channel, a Kalman filter-based algorithm is presented to estimate the time-varying optical scattering channel parameters. Finally, some open issues and challenges in the optical scattering-based V2V technology are presented.

Semi-Decentralized Network Slicing for Reliable V2V Service Provisioning: A Model-Free Deep Reinforcement Learning Approach

Applying of network slicing in vehicular networks becomes a promising paradigm to support emerging Vehicle-to-Vehicle (V2V) applications with diverse quality of service (QoS) requirements. However, achieving effective network slicing in dynamic vehicular communications still faces many challenges, particularly time-varying traffic of Vehicle-to-Vehicle (V2V) services and the fast-changing network topology. By leveraging the widely deployed LTE infrastructures, we propose a semi-decentralized network slicing framework in this paper based on the C-V2X Mode-4 standard to provide customized network slices for diverse V2V services. With only the long-term and partial information of vehicular networks, eNodeB (eNB) can infer the underlying network situation and then intelligently adjust the configuration for each slice to ensure the long-term QoS performance. Under the coordination of eNB, each vehicle can autonomously select radio resources for its V2V transmission in a decentralized manner. Specifically, the slicing control at the eNB is realized by a model-free deep reinforcement learning (DRL) algorithm, which is a convergence of Long Short Term Memory (LSTM) and actor-critic DRL. Compared to the existing DRL algorithms, the proposed DRL neither requires any prior knowledge nor assumes any statistical model of vehicular networks. Furthermore, simulation results show the effectiveness of our proposed intelligent network slicing scheme.

An OFDM-Based Pre-Coded Chaos Shift Keying Transceiver for reliable V2V Transmission

In this correspondence, we propose a novel orthogonal frequency division multiplexing based pre-coded chaos shift keying (OFDM-PC-CSK) transceiver for reliable vehicle-to-vehicle (V2V) communications. In this design, different from traditional OFDM-aided differential chaos shift keying (OFDM-DCSK) systems, we remove the delivery of reference chaotic signals and utilize the diagonalization principle for information transmissions to achieve better reliability than the differential demodulation in OFDM-DCSK. At the transmitter, we construct the chaotic phase vector with chaotic sequences having different initial values, which are then combined with the discrete Fourier transform (DFT) matrix and a conjugate phase matrix of an eigenvalue matrix to compose a precoder. After precoding, the user data are modulated by the chaotic circulant matrix and transmitted over multiple subcarriers. At the receiver, reverse operations are conducted. Subsequently, we derive the theoretical bit error rate (BER) over V2V channels, the spectrum efficiency and the computational complexity. Simulation results validate the theoretical analysis, and demonstrate that the OFDM-PC-CSK system outperforms the counterpart schemes with better reliability and security performances over V2V channels.

NA-SMT: A Network-Assisted Service Message Transmission Protocol for Reliable IoV Communications

Cellular V2X (C-V2X) is an emerging standard that will enable the employment of the 6G Internet of Vehicles (IoV). It is envisaged to provide seamless connectivity, long-range communications, higher data rate transmissions, and centralized and ad hoc modes of communications for vehicular applications. In particular, service applications that require data transmission between vehicles and infrastructure stations known as Road Side Units (RSUs) can greatly benefit from C-V2X. In this paper, we propose a Network-Assisted Service Message Transmission (NA-SMT) protocol that uses Uu and PC5 links of C-V2X to efficiently disseminate service messages from RSUs to the vehicles. In the first phase of the NA-SMT protocol, RSUs select downlink relay vehicles for each service message with the highest Channel Quality Indicator ( $CQI$ ) within a defined reliable range of the destination vehicle. The second phase of the NA-SMT protocol schedules V2V transmission by cooperative sub-channel allocation by the RSUs to reliably disseminate service messages to the destination. Simulation results show that NA-SMT protocol improves packet reception ratio and end-to-end delay of service messages at different channel conditions and service message generation rates.

Channel Modelling and Performance Limits of Vehicular Visible Light Communication Systems

Visible light communication (VLC) has been proposed as an alternative or complementary technology to radio frequency vehicular communications. Front and back vehicle lights can serve as wireless transmitters making VLC a natural vehicular connectivity solution. In this paper, we evaluate the performance limits of vehicular VLC systems. First, we use non-sequential ray tracing to obtain the channel impulse responses (CIRs) for vehicle-to-vehicle (V2V) link in various weather conditions. Based on these CIRs, we present a closed-form path loss expression which builds upon the summation of geometrical loss and attenuation loss and takes into account asymmetrical patterns of vehicle light sources and geometry of V2V transmission. The proposed expression is an explicit function of link distance, lateral shift between two vehicles, weather type (quantified by the extinction coefficient), transmitter beam divergence angle and receiver aperture diameter. Then, we utilize this expression to determine the maximum achievable link distance of V2V systems for clear, rainy and foggy weather conditions while ensuring a targeted bit error rate.

Ray-Based Modeling of Directional Millimeter-Wave V2V Transmissions in Highway Scenarios

Due to the need for larger bandwidth, future mobile networks may primarily operate over millimeter-wave (mmWave) bands. In this regard, one of the central research topics today is mmWave channel modeling. However, highly mobile vehicle-to-vehicle (V2V) mmWave channels pose an open question because conducting measurements may be challenging in this environment. The existing publications on mmWave V2V channel modeling consider line-of-sight (LoS) or single-blocker scenarios. Accordingly, the impact of interference from adjacent vehicles is not taken into account. Conventional analytical models suggest that path loss (PL) strikes equally in all directions whereas measurements are inherently directional, especially in the case of mmWave propagation. Recently, it was proposed to synthesize the omnidirectional PL from directional measurements. However, with this method, the resulting PL is underestimated. Moreover, theoretical models assume that vehicular transceivers follow a certain predetermined distribution, which may not be accurate in real-world scenarios. Several works take into account the directionality of an antenna pattern when the antenna orientation is known. However, the effects of reflection, diffraction, and transmission through obstacles are not considered. One way to avoid these limitations is to use ray-based simulations. However, the ray-launching (RL) approach does not specify any criteria for the directionality of the antenna patterns. Assuming omnidirectional PL appears impractical for mmWave transmission where directional communication is crucial to combat high PL. In this paper, we propose an approach for synthesizing directional PL based on extensive RL modeling. For the obtained directional and omnidirectional PL, we parametrize the close-in (CI) and float-intercept (FI) channel models. As a result of our modeling, from 0.1 to 2.7 for the LoS and from 0.1 to 2.6 for the non-LoS (NLoS) higher PL exponent is observed as compared to the omnidirectional case.

V2V for Vehicular Safety Applications

Low delay and high reliability of safety broadcast messages are critical to future intelligent transport systems (ITS). The main objective of this paper is to propose a novel safety broadcast system model to satisfy the latency and reliability requirements of vehicle-to-vehicle (V2V) applications. Second, using this proposed model, a comprehensive performance evaluation is conducted under different technologies (cellular and IEEE 802.11p) for different vehicle densities in rural and urban scenarios based on a realistic system-level simulator. The performance is further improved by using application layer raptor Q codes as compared to the standard repetition codes proposed in the literature. Results show a coverage improvement of 144 m (rural) and 45 m (urban) using cellular V2V with raptor Q codes as compared to IEEE 802.11p for a target end-to-end delay of 5 ms. This is because IEEE 802.11p suffers from preamble channel estimation especially in urban areas with high collision rate due to the hidden terminal problems. In case of a traffic jam, with higher vehicle density, full duplex (FD) operation is needed for cellular V2V to achieve the target delay. FD operation satisfies the latency requirements even when half the resources are available for V2V transmissions.

A Collaborative Code Dissemination Schemes Through Two-Way Vehicle to Everything (V2X) Communications for Urban Computing

Urban computing creates novel solutions for smart city systems by integrating with ubiquitous sensing, cooperative communication, and data/code collection or dissemination technologies. In this paper, a collaborative code dissemination (CCD) approach with two-way vehicle to everything (V2X) paradigm is proposed by using vehicles act as code mules to disseminate code with fast and low cost style for urban Computing. The main innovation of the CCD-V2X scheme, which is different from the previous code dissemination method, is the use of three cooperative communication methods to speed up the dissemination of code. They are Roadside Smart Devices (RSDs) to vehicle (R2V), vehicle to vehicle (V2V) and R2V combined V2V (R2V-V2V) communications method. The corresponding three code dissemination schemes are proposed as follow: (1) The Code Dissemination with Roadside Smart Device Assisted (short as CDRSDA) scheme is proposed for vehicles to fast disseminate code in which the vehicles can receive code from RSDs that have get update code. (2) A Code Dissemination with V2V Transmission Assisted (short as CDVVA) scheme is proposed to improve the performance of code dissemination by enabling the transmission from code mules that have update code to the code mules that have no update code. (3) The R2V combined V2V (R2V-V2V) scheme is proposed which can further improve the performance by using both R2V and V2V communication method. The CCD scheme has been extensively experimentally verified by using real vehicle trajectory data. The experimental results show that: Compared with the Traditional Scheme, the CDRSDA scheme, CDVVA scheme, R2V-V2V scheme can be increased by up to 35.42%, 35.42%, 34.72%, respectively from the code diffusion coverage performance index, and from the speed performance indicators, the three schemes increased by at least 68.4%, 89.3% and 90.3%.

Hidden Vehicle Sensing via Asynchronous V2V Transmission: A Multi-Path-Geometry Approach

Accurate vehicular sensing is a basic and important operation in autonomous driving. Unfortunately, the existing techniques have their own limitations. For instance, the communication-based approach (e.g., transmission of GPS information) has high latency and low reliability while the reflection-based approach (e.g., RADAR) is incapable of detecting hidden vehicles (HVs) without line-of-sight. This is arguably the reason behind some recent fatal accidents involving autonomous vehicles. To address this issue, this paper presents a novel HV-sensing technology that exploits multi-path transmission from a HV to a sensing vehicle (SV). The powerful technology enables the SV to detect multiple HV-state parameters including position, orientation of driving direction, and size. Its implementation does not even require synchronization like conventional mobile positioning techniques. Our design approach leverages estimated information on multi-path [namely their angles-of-arrival (AoA), angles-of-departure (AoD), and time-of-arrival (ToA)] and their geometric relations. As a result, a complex system of equations or optimization problems, where the desired HV-state parameters are variables, can be formulated for different channel-noise conditions. The development of intelligent solution methods ranging from least-square estimator to disk/box minimization yields a set of practical HV-sensing techniques. We study their feasibility conditions in terms of the required number of paths. Furthermore, practical solutions, including sequential path combining and random directional beamforming, are proposed to enable HV-sensing given insufficient paths. Last, realistic simulation of driving in both highway and rural scenarios demonstrates the effectiveness of the proposed techniques. In summary, the proposed technique will enhance the capabilities of existing vehicular sensing technologies (e.g., RADAR and LIDAR) by enabling HV-sensing.

Performance Analysis and Power Control for Multi-Antenna V2V Underlay Massive MIMO

Device-to-device (D2D) communication has been considered a promising solution for vehicle-to-vehicle (V2V) communication. In V2V underlay massive MIMO cellular networks, V2V has the potential to increase spectral efficiency (SE) by reusing cellular spectrum. However, the interference between cellular and V2V transmissions may be serious if not designed properly. In this paper, we first derive the analytical lower bounds on multi-antenna V2V and cellular SEs under both perfect and imperfect channel state information for maximum-ratio combining and zero-forcing detection. Using these lower bounds, performance analysis is given to provide more insights and guide practice. Then, we formulate a large-scale fading-based power control problem to guarantee the SE constraints of V2V pairs while maximizing the sum SE of cellular users. Moreover, the geometric programming technique is applied to solve the optimal power control problem. Numerical results show that the proposed SE-constrained power control scheme can greatly increase the reliability and achievable SE of V2V communications while the achievable sum SE is slightly degraded, compared with the fixed maximum power scheme.

A Fuzzy‐Rule Based Data Delivery Scheme in VANETs with Intelligent Speed Prediction and Relay Selection

Data delivery in vehicular networks (VANETs) is a challenging task due to the high mobility and constant topological changes. In common routing protocols, multihop V2V communications suffer from higher network delay and lower packet delivery ratio (PDR), and excessive dependence on GPS may pose threat on individual privacy. In this paper, we propose a novel data delivery scheme for vehicular networks in urban environments, which can improve the routing performance without relying on GPS. A fuzzy‐rule‐based wireless transmission approach is designed to optimize the relay selection considering multiple factors comprehensively, including vehicle speed, driving direction, hop count, and connection time. Wireless V2V transmission and wired transmissions among RSUs are both utilized, since wired transmissions can reduce the delay and improve the reliability. Each RSU is equipped with a machine learning system (MLS) to make the selected relay link more reliably without GPS through predicting vehicle speed at next moment. Experiments show the validity and rationality of the proposed method.

A Latency and Coverage Optimized Data Collection Scheme for Smart Cities Based on Vehicular Ad-hoc Networks.

Using mobile vehicles as “data mules” to collect data generated by a huge number of sensing devices that are widely spread across smart city is considered to be an economical and effective way of obtaining data about smart cities. However, currently most research focuses on the feasibility of the proposed methods instead of their final performance. In this paper, a latency and coverage optimized data collection (LCODC) scheme is proposed to collect data on smart cities through opportunistic routing. Compared with other schemes, the efficiency of data collection is improved since the data flow in LCODC scheme consists of not only vehicle to device transmission (V2D), but also vehicle to vehicle transmission (V2V). Besides, through data mining on patterns hidden in the smart city, waste and redundancy in the utilization of public resources are mitigated, leading to the easy implementation of our scheme. In detail, no extra supporting device is needed in the LCODC scheme to facilitate data transmission. A large-scale and real-world dataset on Beijing is used to evaluate the LCODC scheme. Results indicate that with very limited costs, the LCODC scheme enables the average latency to decrease from several hours to around 12 min with respect to schemes where V2V transmission is disabled while the coverage rate is able to reach over 30%.