Vehicle Communication Research Articles

Design of Internet of Vehicles Routing Protocol based on Prior Probability

With the continuous development of the interactive system and the wireless heterogeneous network, the intelligent transportation system plays a key role in the modern transportation, especially in the construction of the Internet of vehicles. The important position of routing protocol in building the vehicle communication network is self-evident. V2V (Vehicle to Vehicle) network also has irreplaceable advantages in eliminating traffic accidents and reducing the harm of traffic accidents. Therefore, it is very necessary to study the optimization of the routing protocol. However, in the previous vehicle since the organization network routing protocol research, mostly focus on the method of data fusion, for the improvement of the conflict in the routing protocol, and through machine learning Q algorithm to optimize the real-time transmission model, etc., ignore the different degree of preference for different information. With the help of this kind of information, the routing protocol can be optimized, and the complexity of the routing protocol can be further reduced. Improve the efficiency of Internet of Vehicles communication.

Robust beamforming design for UAV communications based on integrated sensing and communication

Integrated sensing and communication (ISAC) has been a promising technique in various wireless communication applications. In this paper, we investigate a beamforming design method based on ISAC waveforms in unmanned aerial vehicle (UAV) communications. An integrated state prediction and beamforming design framework is presented. We utilize the target states from sensing algorithms to improve the prediction performance. Based on the predicted states, we formulate the mathematical form of the communication interruption probability. To enhance the beamforming performance, we propose a design approach that satisfies both sensing and communication metrics and the communication interruption constraints. We show that the proposed method achieves robust communication under the integrated state prediction and beamforming design framework. Simulation results show that by using the ISAC signal, our method significantly lowers the communication interruption probability in the beamforming process and achieves better communication performance.

Sub-Connected Hybrid Precoding and Trajectory Optimization Using Deep Reinforcement Learning for Energy-Efficient Millimeter-Wave UAV Communications

In this letter, a sub-connected hybrid precoding system was designed to realize energy-efficient millimeter-Wave (mmWave) unmanned aerial vehicle (UAV) communications. Considering the limited capacity of the UAV battery, the system was jointly optimized with a UAV trajectory to increase the energy efficiency of the UAV under quality-of-service (QoS) and power budget constraints. The dynamic motion of the UAV changes the channel condition between the UAV and terrestrial users over time. Hence, a joint optimization problem was formulated as a non-convex and time-sequential domain, solved using deep reinforcement learning (DRL). The performances of the proposed scheme and a fully-connected hybrid precoding scheme were compared in terms of energy efficiency and show higher results.

A 3D Non-Stationarity and Consistency Model for Cooperative Multi-Vehicle Channels

In this paper, based on the irregular shaped geometry-based stochastic modeling (IS-GBSM) approach, a novel three-dimensional (3D) model for cooperative multi-vehicle communication channels is developed. By employing the cooperative communication technology, multi-vehicles can form a platoon to cooperatively transmit data streams, which can achieve distributed multiple-input multiple-output (MIMO) system performance. The proposed cooperative IS-GBSM distinguishes static and dynamic clusters, where continuously arbitrary vehicular movement trajectories (VMTs) of dynamic clusters and multi-vehicles are imitated. A new method, which combines frequency-dependent path gain and spectral clustering algorithm in machine learning (ML), is proposed to model cooperative space-time-frequency (S-T-F) non-stationarity with cooperative time-space (T-S) consistency. Based upon Poisson and Bernoulli processes, the developed method can also model the disappearance of links/sub-channels due to the destruction of transceivers by missiles in an optional manner. Therefore, the proposed cooperative IS-GBSM can further mimic an actual military cooperative communication scenario, i.e., cooperative multiple-unmanned ground vehicle (multi-UGV) communications for Mosaic Warfare. From the proposed cooperative IS-GBSM, typical channel statistics are derived. Simulation results show that cooperative S-T-F non-stationarity with cooperative T-S consistency is mimicked and the influences of vehicular traffic density (VTD) and VMT on channel statistics are investigated. Finally, simulation results fit well with the ray-tracing-based results, confirming the accuracy of the proposed cooperative IS-GBSM.

IRS-Enabled Secure G2A Communications for UAV System With Aerial Eavesdropping

As one of the most promising new technologies in the upcoming 6G communication, intelligent reflecting surface (IRS) can significantly improve the reliability of wireless transmission and deal with the security threat in the presence of eavesdroppers by smartly reconfiguring the wireless propagation environment. However, existing research mainly focuses on terrestrial communication networks. In contrast, there is not much research on IRS-aided secure unmanned aerial vehicle (UAV) communication problems, especially for ground-to-air (G2A) communication scenarios with air eavesdroppers that will bring greater security threats. In this article, we consider an IRS-assisted G2A communication network with an altitude-dependent Rician fading channel, where the ground base station (BS) transmits legitimate information to the UAV user in the presence of air eavesdroppers. The transmission power, active and passive beamforming, and UAV 3-D trajectory are jointly optimized to maximize the average secrecy rate. Due to the nonconvexity of the optimization problem, the block coordinate descent method is used to solve it. Specifically, the overall optimization problem is divided into four subproblems, and nonconvex subproblems are solved by successive convex approximation and semidefinite relaxation algorithms. Simulation results show that the proposed alternating optimization algorithm has a good convergence performance and can increase the average secrecy rate by 28% compared with the scenario without IRS assistance.

Channel capacity on visible light communication

As a typical application scheme of OWC (Optical Wireless Communication), VLC (Visible Light Communication) plays a more vital role in our daily life. It is expected to be used in the fields of vehicle communication, 6G network, underwater wireless communication, etc. Although so many achievements on VLC have been obtained, researchers still have some obstacles to overcome. Channel capacity as an important quota to evaluate communication quality deserves researchers to study further. This paper not only discusses the basic theory and combines the results of some recent studies on visible light channel capacity, but also highlights a promising application prospect and advantages over other communication methods, including RF (radio frequency), and fiber optical. This paper draws a conclusion that VLC is mainly used in indoor and short-distance communication. There is still a long way to apply VLC in long-distance communication and overcome the fading problem.

Wideband Low-RCS Step-Type Cavity-Backed Stacked Dielectric Resonator Antenna for Unmanned Aerial Vehicle Communications

This letter explains the design of a cavity-backed stacked dielectric resonator antenna (SDRA) with wider impedance bandwidth (IBW) and improved gain for C-band and S-band applications. The antenna mainly comprises of four dielectric resonators stacked above one another of varying size and shape. Combining the SDRA with step-type cavity and conformal radome forms the final antenna. The antenna provides a wider IBW of 103% (2.07–6.55 GHz), dual-band axial ratio bandwidth of 27.90% (2.38–3.15 GHz) and 16% (5.01–5.88 GHz), respectively. The antenna provides a maximum gain of 9 dBi. The final cavity-backed antenna offers a good radar cross-section reduction ranging from 2–8 GHz for both normal and oblique incidence angles. Hence the proposed SDRA with the dual-layer cavity is applicable for unmanned aerial vehicle communications.

Intelligent Road Management System for Autonomous, Non-Autonomous, and VIP Vehicles

Currently, autonomous vehicles, non-autonomous vehicles, and VIP (emergency) autonomous cars are using intelligent road management techniques to interact with one another and enhance the effectiveness of the traffic system. All sorts of vehicles are managed and under control using the intersection management unit approach. This study focuses on transportation networks where VIP cars are a major disruption, accounting for 40% of accidents and 80% of delays. Intelligent Mobility (IM) is a strategy promoted in this study that proposes setting up intelligent channels for all vehicle communication. As part of its function, the IM unit keeps tabs on how often each junction is used so that it may notify drivers on traffic conditions and ease their workload. The suggested layout may drastically cut average wait times at crossings, as shown in SUMO simulations. The entrance of a VIP car should disrupt all traffic, but the IM (intersection management) unit effectively manages all traffic by employing preemptive scheduling and non-preemptive scheduling techniques for all types of vehicles. We are employing Nishtar roads, the M4 motorway, Mexico, and Washington roads in our scenario. In comparison to all other routes, the simulation results demonstrate that the Washington road route is better able to manage all vehicle kinds. Washington’s traffic delays for 50 cars of all sorts are 4.02 s for autonomous vehicles, 3.62 s for VIP autonomous vehicles, and 4.33 s for non-autonomous vehicles.

Sum Rate Maximization for Intelligent Reflecting Surface-Assisted UAV-Enabled NOMA Network

In the next-generation network, intelligent reflecting surface (IRS), non-orthogonal multiple access (NOMA), and simultaneous wireless information and power transfer (SWIPT) are promising wireless communication techniques to effectively improve system sum rates. In traditional unmanned aerial vehicles (UAV) communication systems, the sum rate and coverage are greatly affected when there is an occlusion on the direct transmission link. To solve this problem, the IRS technology is introduced to improve the poor channel conditions. However, most of the previous research on IRS-assisted UAV to optimize system sum rate only considers frameworks that utilize the partially joint-combining techniques of IRS, NOMA, and SWIPT. In this paper, in order to further improve the sum rate of the system, we simultaneously integrate IRS, NOMA, and SWIPT technologies and establish a sum rate maximization optimization problem when the direct link is blocked. Then, an alternative optimization (AO) algorithm based on the maximizing system sum rate is proposed to solve the non-convex optimization problem, in which the IRS location and phase, the reflecting amplitude coefficient, UAV forwarding altitude, and power splitting factor are considered. To let the non-convex and non-linear function be transformed into a convex one, we first use an iterative approach to optimize the position of the IRS. After that, an optimization problem is constructed to maximize the system sum rate with the constraints of the IRS phase shifts, successful successive interference cancellation (SIC), maximum transmit power of base station (BS), and UAV. Numerical results show that the proposed algorithm outperforms the traditional orthogonal multiple access (OMA) and algorithms without IRS-assisted links in terms of the system sum rate.

Cybersecurity threats mitigation in Internet of Vehicles communication system using reliable clustering and routing

Security and privacy while considering vehicular networks are important research topics. Appropriate clustering algorithms enable long-distance communications in highly dynamic Internet of Vehicles (IoV) systems. IoV system QoS requires constant, low-cost clustering. However, to prevent malicious behaviors, sensitive vehicle networks need an access control system for data security and privacy. The existing solutions suffered from higher complexity and complete reliability. Thus, the main objective of this paper is to propose a lightweight routing technique to express network access control policies to ensure higher reliability with minimum latency and overhead. The Trust Aware Clustering-based Routing Protocol (TACR) solves IoV communication security and reliability issues while reducing computational costs and latency. TACR functionality relies on trust-management methods for optimal Cluster Head (CH) and relay (data forwarder) selection. Each car's direct and indirect trust scores are calculated during clustering. The fitness function of the Ant Colony Optimisation (ACO) algorithm uses each vehicle's hybrid trust value. The ACO algorithm chooses stable vehicles for the best CH. In the routing phase, we select forwarding relays and apply trust-based access control rules to ensure safe and reliable data transmission. Fuzzy logic selects the optimal relay node and sets access control rules based on trust evaluation. The trust criteria consider each vehicle's expertise, knowledge, and suggestions throughout. The simulation findings reveal that the TACR protocol outperforms state-of-the-art protocols significantly. Average throughput and PDR are increased by 14.17 % and 14.34 % using TACR, respectively. Average delay and communication overhead were reduced by 33.84 % and 14.28 % using the TACR protocol, respectively.

Jacobian linear regression and Tate Bryant Euler angle enabled autonomous vehicle LiFi communication sustained IOT

Artificial Intelligence (AI) and the constant paradigm shift in road traffic have led to a need for significant improvement in road safety to minimize traffic accidents. LiFi helps minimize accidents by transmitting data between multiple vehicles (i.e. Vehicle-to-Vehicle (V2V)) and between vehicles and infrastructure (i.e. Vehicle-to-Infrastructure (V2I)) without interference. LiFi uses light to transmit data between devices or vehicles, which ensures efficient data transmission speed and is therefore considered a safe technology. A method called Deep Jacobian Regression and Tate Bryant Euler Recommendation (DJR-TBER) is proposed in this paper based on V2V and V2I autonomous vehicle communication. The proposed method DJR-TBER consists of an input layer, four hidden layers and finally an output layer. Sensors are first used to obtain the information. A linear regression-based speed evaluation model is developed and followed by a Jacobi matrix-based distance evaluation model in the hidden layer. The third hidden layer by developing a distance evaluation model. The use of Laplacian function ensures secure V2I communication for the autonomous vehicle. Finally, a Tate-Bryant-Euler angle-based model for emergency handling is proposed in the hidden layer to optimally consider the aspect of braking in emergency situations and thus increase driving safety.

Hepatocyte-derived exosomal miR-146a-5p inhibits hepatic stellate cell EMT process: a crosstalk between hepatocytes and hepatic stellate cells

Recently, Salidroside (Sal) has been demonstrated to suppress hepatic stellate cell (HSC) activation, a crucial event for liver fibrosis. Moreover, Sal has been reported to decrease hepatocyte injury. A growing number of reports have indicated that the crosstalk between hepatocytes and HSCs is very crucial for liver fibrosis development. Whether Sal-treated hepatocytes could inhibit HSC activation is unclear. Exosomes, as vital vehicles of intercellular communication, have been shown to transfer cargos between hepatocytes and HSCs. Herein, we aimed to investigate the roles of exosomal miRNAs from Sal-treated hepatocytes in HSC activation as well as liver fibrosis. Our results showed that Sal suppressed carbon tetrachloride (CCl4)-induced liver fibrosis in vivo. HSC activation as well as cell proliferation was repressed in HSCs co-cultured with Sal-treated hepatocytes. Interestingly, miR-146a-5p was up-regulated by Sal in CCl4-treated mice. Also, enhanced miR-146a-5p was found in hepatocytes isolated from Sal-treated CCl4 mice and hepatocyte-derived exosomes. Notably, hepatocyte exosomal miR-146a-5p contributed to HSC inactivation. Inhibiting miR-146a-5p in hepatocyte exosomes resulted in reduced E-cadherin (E-cad) and increased desmin in HSCs, indicating that miR-146a-5p caused HSC inactivation via epithelial-mesenchymal transition (EMT). miR-146a-5p inhibition-mediated HSC activation and EMT process were blocked down by loss of EIF5A2. Further studies revealed that EIF5A2 was a target of miR-146a-5p. Furthermore, exosomes with miR-146a-5p overexpression inhibited liver fibrosis in CCl4 mice. Collectively, exosomal miR-146a-5p from Sal-treated hepatocytes inhibits HSC activation and liver fibrosis, at least in part, by suppressing EIF5A2 and EMT process.

OF-FSE: An Efficient Adaptive Equalization for QAM-Based UAV Modulation Systems

Quadrature amplitude modulation (QAM) is one of the essential components of unmanned 1 aerial vehicle (UAV) communications. However, the output signal accuracy of QAM deteriorates dramatically and even collapses in the case of UAVs in a harsh channel environment. This is due to the fractionally spaced equalization based on the multi-modulus blind equalization algorithm being implemented prior to carrier synchronization in QAM-based UAV modulation systems. The carrier frequency offset from the harsh channel signal thus contributes to the significantly degraded performance of MMA by suffering the fractionally spaced equalization. Therefore, in this paper, a novel offset feedback fractionally spaced equalization architecture for UAVs to eliminate the carrier frequency offset is first proposed. In this architecture, the carrier frequency offset allows estimated and incorporation into the input signal of fractionally spaced equalization to compensate for the offset. Moreover, a new multi-modulus decision-directed algorithm is presented for the novel architecture to improve the received signal accuracy of UAVs further. It enables adaptive optimization of the convergence process in accordance with the dynamic UAV communication environment employing the multi-modulus blind equalization algorithm and decision-directed blind equalization algorithm (MDD). Simulation results demonstrate the effectiveness of the OF-FSE framework in enabling the QAM-based UAV modulation systems operation in harsh channel scenarios. Moreover, the performance of the presented new MDD algorithm compared with baseline approaches is also confirmed.

Advanced adaptive cruise control and vehicle to vehicle communication using LiDAR

The ACC system is a type of driving assistance that helps reduce the burden on a driver. It operates by controlling the vehicle's acceleration and deceleration to maintain a certain speed or avoid a collision. In order to improve the accuracy of its operation, this paper proposes a method that uses radar to detect the presence of the preceding vehicle. It also takes into account the vehicle sideslip angle and the distance between the two vehicles. The proposed method can help improve the driving stability. In addition, we are currently using the vehicle-to-vehicle communication technology in our system. This type of inter-vehicle communication is not widely used yet and is a new paradigm in the field of communication. One of the main advantages of this type of communication is that it doesn't rely on third-party networks such as cellular networks. Despite the various advantages of this technology, it still has a long way to go before it can be fully deployed. The increasing number of operators who are interested in this technology has led to the development of more effective and efficient V2V solutions. This paper aims to provide a comprehensive analysis of the various challenges and benefits of this type of communication. The paper also focuses on the various security issues that are associated with this technology. One of these is the potential interference. This issue can prevent the system from operating efficiently. In order to address this issue, the paper suggests an improved architectural solution that can help prevent this type of interference. The proposed work also proposes a standardized space for the various components of the V2V system. This will allow them to easily integrate into the system. Vehicle-to-Vehicle communication is a type of wireless communication that is designed to allow cars to talk to one another.

Energy Efficient Resource Allocation for UAV-Served Energy Harvesting-Supported Cognitive Industrial M2M Networks

In this letter, the resource allocation problem for Energy Harvesting-supported Cognitive Industrial Machine-to-Machine (EH-CI-M2M) network underlaying Unmanned Aerial Vehicles (UAVs) communication is investigated with the objective of maximizing the average energy efficiency by jointly considering the EH time slot assignment, transmit power control and bandwidth allocation under the constraints of Quality of Service (QoS) and the available energy status of the EH-CMNet devices. Nevertheless, the optimization problem is difficult to be tackled directly since it is non-convex and NP-hard. We first transform the primitive objective problem into a convex form equivalently by non-linear fractional programming and variable relaxation approach. Based on Dinkelbach and Lagrangian theory, an iterative algorithm is proposed to solve the optimization problem. Extensive simulation results demonstrate that the proposed scheme outperforms the benchmark schemes in terms of energy efficiency in different network settings.

A Novel Geometry-Based 3-D Wideband Channel Model and Capacity Analysis for IRS-Assisted UAV Communication Systems

Intelligent reflecting surface (IRS) composed of a large number of low-cost passive reflecting elements has attracted significant attention from communication communities because of its ability to substantially improve the communication performance. In this paper, the aperture area and radiation pattern of the IRS reflecting elements are considered, and a novel geometry-based three-dimensional (3-D) wideband channel model is proposed for IRS-assisted unmanned aerial vehicle (UAV) communication systems. In the proposed model, the reflection phase is designed by jointly considering the aperture area of the IRS reflecting element and the propagation phases among UAV, IRS, and receiver (Rx), each IRS reflecting element is modeled as an anomalous reflector instead of a specular reflector, and large-scale IRS reflecting elements can jointly beamform the signal in a desired direction. Based on the proposed model, the effects of arbitrary trajectory of UAV and the number and the size of passive reflecting elements on channel statistical characteristics are considered, and the average received signal power and the ergodic sum capacity, which consider the impacts of the number and the size of passive reflecting elements, are also investigated. Furthermore, the path loss of the IRS-assisted link, which is in inverse proportion to the square of aperture area of IRS reflecting elements, is derived, and it coincides with the measured results in real outdoor scenarios. Analysis shows that the communication performance can be enhanced by increasing the number and the size of IRS reflecting elements, and it is validated by numerical results and Monte-Carlo simulation results.

Multi-Objective Optimization Approaches for Physical Layer Secure Communications Based on Collaborative Beamforming in UAV Networks

Unmanned aerial vehicle (UAV) communications and networks are promising technologies in the forthcoming 5G/6G wireless communications. However, they have challenges for realizing secure communications. In this paper, we consider to construct a virtual antenna array consists UAV elements and use collaborative beamforming (CB) to achieve the UAV secure communications with different base stations (BSs), subject to the known and unknown eavesdroppers on the ground. To achieve a better secure performance, the UAV elements can fly to optimal positions with optimal excitation current weights for performing CB transmissions. However, this leads to extra motion energy consumption. We formulate a physical layer secure communication multi-objective optimization problem (MOP) of UAV networks to simultaneously improve the total secrecy rates, total maximum sidelobe level (SLL) and total motion energy consumption of UAVs by jointly optimizing the positions and excitation current weights of UAVs, and the order of communicating with different BSs. Due to the complexity and NP-hardness of the formulated MOP, we propose an improved multi-objective dragonfly algorithm with chaotic solution initialization and hybrid solution update operators (IMODACH) and a parallel-IMODACH (P-IMODACH) to solve the problem. Simulation results verify that the proposed approaches can effectively solve the formulated MOP and it has better performance than some other benchmark algorithms and approaches. Moreover, some unexpected circumstances are considered and discussed.

Idle-Less Slotted ALOHA Protocol for Drone Swarm Identification

In this paper, we modify the slotted ALOHA protocol to quickly identify a swarm of drones in unmanned aerial vehicle (UAV) communication environments. Considering the unreliable UAV channel and the fundamental tradeoff between collision and idle slots in slotted ALOHA, we propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">idle-less slotted ALOHA</i> (ILS-ALOHA) protocol that detects and utilizes idle slots to transmit control messages to drones. In ILS-ALOHA, the ground control station recognizes the idle slot promptly at the beginning of each slot and then transmits acknowledgment (ACK) and beacon messages during the remaining period of the idle slot. Therefore, it can control the access of the drone swarm more adaptively and feed back the ACK to the drones more frequently in an error-prone UAV environment without allocating additional slots. The analysis and simulation results show that the proposed ILS-ALOHA protocol outperforms the conventional slotted ALOHA-based protocols in terms of identification time, channel throughput, and probability of successful identification.

Cooperative Transportation of UAVs Without Inter-UAV Communication

In this article, a leader–follower system is developed for cooperative transportation. To the best of our knowledge, this is the first work in which interunmanned aerial vehicle communication is not required and the reference trajectory of the payload can be modified in real time, so that it can be applied to a dynamically changing environment. To track the modified reference trajectory in real time under the communication-free condition, the leader–follower system is considered as a nonholonomic system in which a controller is developed for the leader to achieve an asymptotic tracking of the payload. To eliminate the need to install force sensors, unscented Kalman filters (UKFs) are developed to estimate the forces applied by the leader and the follower. Stability analysis is conducted to prove that the tracking error of the closed-loop system is asymptotically stable; however, the dynamics of the UKF estimators is not considered in the closed-loop system, which is assumed to be a deterministic system for the controller. The experiments show that the controllers of the leader and the follower can work in the real world, but the tracking errors are affected by the disturbance of airflow in a restricted space.

Research on evaluation of emergency communication vehicle cabin’s internal environment design based on q-rung orthopair fuzzy information

The design of the emergency communication vehicle is centered around the overall layout, which is characterized by the fact that the vehicle chassis is mostly purchased parts and is equipped with an insulated and insulated box, mainly used for instrument operation and equipment placement. The emergency communication vehicle cabin environment includes: cabin seats; seat cover; control cabinet, including operation keyboard, display screen, etc; The ceiling and carpet that make up the interior space of the car; Storage box for storage equipment; tool cabinet; cabin control system facilities; Fire prevention and extinguishing facilities in the cabin; various handrails, coat racks, short ladders, lighting facilities, etc. inside the car. The design of emergency communication vehicle interior environment involves multiple disciplines, and the selection of design schemes requires scientific and reasonable evaluation methods to select reasonable schemes for optimization design. The evaluation of emergency communication vehicle cabin’s internal environment design is classical multiple attribute decision making (MADM) problems. In this article, based on bidirectional projection and grey relational projection measure, we shall propose some projection models with q-rung orthopair fuzzy sets (q-ROFSs). First of all, the definition of q-ROFSs is introduced. Furthermore, some projection models with q-ROFSs are proposed based on the bidirectional projection and grey relational projection model. Based on developed weighted projection models, the MADM model is established and all computing steps are simply depicted. Finally, a numerical example for evaluation of emergency communication vehicle cabin’s internal environment design is given to illustrate this new model and some comparisons are conducted to illustrate advantages of the new built method.