Decoding algorithm with multiple features based on optical camera communication system

The performance of decoding algorithm is one of the important influential factors to determine the communication quality of optical camera communication (OCC) system. In this paper, we first propose a decoding algorithm with adaptive thresholding based on the captured pixel values under an ideal environment, and then we further propose a decoding algorithm with multiple features, which is more suitable under the existence of the interference of light sources. The algorithm firstly determines the light-emitting diode (LED) array profile information by removing the interfering light sources through geometric features, and then identifies the LED state by calculating two grayscale features, the average gray ratio (AGR) and the gradient radial inwardness (GRI) of the LEDs, and finally obtains the LED state matrix. The experimental results show that the bit error ratio (BER) of the decoding algorithm with multiple features decreases from 1×10-2 to 5×10-4 at 80 m.     

Performance of OFDM-FSO link with analog network coding

Optical camera communication (OCC) can be considered a convenient and versatile short-range communication technology within the framework of optical wireless communications. As OCC suffers from low data transmission rate in comparison with very high-speed modulation of light emitting diodes (LEDs), it imposes major limitations on an optical camera-based vehicle-to-vehicle (V2V) communication. This paper presents an OCC-based high-speed V2V using a distinct capturing strategy called selective capture (SC). Experiments were performed to verify the proposed SC-based V2V based on a Raspberry Pi camera module (RaspiCam). The SC was performed with template matching technique on the RaspiCam module. The module enables both the selection of resolution and the capturing of vehicle taillights only from the full camera capture frame. As the transmitter, a \(4\times 4\) red LED array was employed as the taillights of a vehicle. It is found that the use of SC to capture the taillights effectively increases the capture speed of RaspiCam from 120 frames per second (fps) to 435 fps, yielding an efficient and high-speed V2V with flicker-free taillights. In addition, the proposed SC-V2V with increased capture speed provides a data rate of up to 3.456 kbps and achieves acceptable bit error rate performance at a distance of up to 175 cm.     

Demodulation algorithm with movement robustness for optical camera communications

Due to the low sampling rate of the camera based receiver, the movement of communication devices becomes a factor that cannot be ignored in the demodulation of optical camera communications (OCC). In this paper, we propose the block matching demodulation (BMD) algorithm, which is a movement-robust and optical-interference-resistant demodulation method for OCC. The BMD algorithm can resist the movement of communication devices by locating communication light source per image and immunize optical interference by utilizing the fast time-varying characteristic of communication light source. Moreover, in order to enhance the reliability of communication systems, BMD algorithm and Manchester decoding are combined to realize error detection and correction without increasing the overhead of encoding. Finally, we build a practical mobile OCC system to test the performance of BMD algorithm. Experimental results show that the BMD algorithm can resist the movement of communication devices effectively and achieve reliable demodulation.     

Implementation and decoding method of OCC system based on MIMO

The application of high-frame-rate cameras as well as the complex image processing techniques will lead to a series of problems, such as high system cost and long transmission delay. In this paper, we introduce narrow-band filtering technology to reduce the impact of optical noise and reduce the complexity of image processing from the physical level. We also introduce multiple-input multiple-output (MIMO) technology into the optical camera communication (OCC) system to increase system transmission rate, and propose a light emitting diode (LED) array decoding algorithm based on the directional projection method to reduce the system delay. By accumulating the target pixel values in each row and column of the image, the proposed method then determines the position and boundary of the detected target to distinguish the target area from the background. Experimental results indicate that the communication distance can reach up to 5.5 m without error bits detected. When the LED array at the transmitter of this system flashes at a frequency of 12 Hz, the transmission rate can reach 126.32 bit/s.     

Multi-level modulation scheme based on PAK algorithm for optical camera communications

In an optical camera communication (OCC) system, multi-level modulation is essential for data rate enhancement with the finite frame rate of the receiving camera constraint, where the K-means algorithm is widely used as a thresholding scheme. The result of K-means clustering is sensitive to initial cluster centers. In this paper, we propose a multi-level modulation scheme utilizing the pilot-aided K-means (PAK) algorithm. PAK algorithm innovates in both obtaining the state of the stripes propagated through the optical channel under different environments and overcoming the susceptibility of K-means. Our scheme could prompt data rate and improve the performance of OCC. Finally, non-orthogonal multiple access (NOMA) pattern is designed based on the proposed scheme to achieve multiplex communications.     

基于多进制检测的可见光多层成像通信系统

为适应多用户需求,提出了一种基于多进制检测的可见光多层成像通信系统。依不同优先权将发送信息加载在分集程度不同的各层发送单元上。优先权越高,分集程度越高,信息的鲁棒性相对较高但传输速率较低;相反的,优先权越低,复用程度越高,信息的传输速率相对较高但鲁棒性较低。结合系统模型,利用基于最大似然检测的多进制检测算法实现了信息提取。最后,通过仿真实验验证了系统的可行性。在保证系统检测性能和算法复杂度相对较低的同时,实现了多层成像通信系统的稳定传输。     

Location prediction algorithm for a nonlinear vehicular movement in VANET using extended Kalman filter

Vehicular ad-hoc network (VANET) is an essential component of the intelligent transportation system, that facilitates the road transportation by giving a prior alert on traffic condition, collision detection warning, automatic parking and cruise control using vehicle to vehicle (V2V) and vehicle to roadside unit (V2R) communication. The accuracy of location prediction of the vehicle is a prime concern in VANET which enhances the application performance such as automatic parking, cooperative driving, routing etc. to give some examples. Generally, in a developed country, vehicle speed varies between 0 and 60 km/h in a city due to traffic rules, driving skills and traffic density. Likewise, the movement of the vehicle with steady speed is highly impractical. Subsequently, the relationship between time and speed to reach the destination is nonlinear. With reference to the previous work on location prediction in VANET, nonlinear movement of the vehicle was not considered. Thus, a location prediction algorithm should be designed by considering nonlinear movement. This paper proposes a location prediction algorithm for a nonlinear vehicular movement using extended Kalman filter (EKF). EKF is more appropriate contrasted with the Kalman filter (KF), as it is designed to work with the nonlinear system. The proposed prediction algorithm performance is measured with the real and model based mobility traces for the city and highway scenarios. Also, EKF based prediction performance is compared with KF based prediction on average Euclidean distance error (AEDE), distance error (DE), root mean square error (RMSE) and velocity error (VE).     

Research on mobile phone swaying and receiving position in optical camera communication

Optical camera communication (OCC) is gaining increasing attention in researches. However, applying it to a real-world scenario may encounter many practical problems, such as the communication device may sway or there may be a position offset between the camera and the light source, which may distort the stripes and make the message unreceivable. To this end, we propose pilot-based stripe area estimation (P-SAE) decoding algorithm, and design a pixels rearrangement (PR) scheme and stripes separation logic decision method (SSLD) to improve the decoding performance under sway or receiving position offset. Finally, we build a practical OCC system and carry out experiments to verify the effectiveness of the decoding algorithm. Experimental results show that the lightweight decoding algorithm can resist the impact of smartphone sway and receiving position offset on decoding accuracy.     

Generating routes for autonomous driving in vehicle-to-infrastructure communications

The study of vehicular networks has attracted considerable interest in academia and the industry. In the broad area, connected vehicles and autonomous driving are technologies based on wireless data communication between vehicles or between vehicles and infrastructures. A Vehicle-to-Infrastructure (V2I) system consists of communications and computing over vehicles and related infrastructures. In such a system, wireless sensors are installed in some selected points along roads or driving areas. In autonomous driving, it is crucial for a vehicle to figure out the ideal routes by the communications between its equipped sensors and infrastructures then the vehicle is automatically moving along the routes. In this paper, we propose a Bezier curve based recursive algorithm, which effectively creates routes for vehicles through the communication between the On-Board Unit (OBU) and the Road-Side Units (RSUs). In addition, this approach generates a very low overhead. We conduct simulations to test the proposed algorithm in various situations. The experiment results demonstrate that our algorithm creates almost ideal routes.     

车路通信环境下TD-LTE无线资源调度建模与仿真

为了改善现有车路通信方式覆盖范围小、交付时延大和传输速率低的现状,将TD-LTE技术引入车路通信接入系统。提出了基于TD-LTE的车路通信接入系统无线资源调度模型;构建了车路通信仿真场景,对比例公平算法、指数比例公平算法和改进的最大权重时延优先算法进行性能分析。仿真结果表明:车辆低速移动场景下,系统负载较低时指数比例公平算法性能较好,系统负载较高时改进的最大权重时延优先算法表现更优,比例公平算法不适合车路通信多媒体业务流调度;车辆高速移动场景下,三者都不适合车路通信多媒体业务流调度。     

车联网中的协同通信平均传输时间计算

车联网(Internet of Vehicles,Io V)是智能交通和通信领域的热点课题,协同通信算法的研究是Io V通信的重要技术之一。针对Io V环境下因通信拓扑结构快速变化导致数据信号利用单一通信方式难以高效传输的问题,提出Io V环境下协同通信算法,利用车对车(Vehicle-to-Vehicle,V2V)和车对路(Vehicle-to-Infrastructure,V2I)协同通信方法,对目标数据从请求到完成的平均传输时间进行了理论分析和推导。仿真结果表明,该算法的传输效率比基于移动边缘计算(Mobile Edge Computing,MEC)车联网协作传输算法提升40%,比基于分簇V2X车载广播传输算法提升25%;该算法的平均传输时间随着路侧单元(Road Side Unit,RSU)缓存概率从0.5增加至1可提高9%,随着车辆缓存概率从0.5增加至1可提高46%。     

面向车联网通信的OTFS信号检测算法综述

车联网(Vehicle to Everything, V2X)通信被认为是未来无线通信网络最重要的应用之一。然而,车辆在高速移动时引起的高多普勒频移会严重恶化V2X通信链路的性能。正交时频空(Orthogonal Time Frequency Space, OTFS)调制技术可以将时间和频率选择性信道转换为时延-多普勒(Delay-Doppler, DD)域的非选择性信道,从而显著提高无线通信系统在高移动性场景下的性能,在V2X通信中具有重要的应用价值。但OTFS调制技术极大地增加了系统接收端的复杂度,研究低复杂度信号检测算法成为了新一代无线通信系统采用OTFS调制的关键问题之一。为此,综述了面向车联网V2X通信的OTFS信号检测算法。首先介绍了OTFS系统模型,然后概述了现有的低复杂度OTFS信号检测算法,并将其分为线性检测算法、消息传递(Message Passing, MP)检测算法及其改进算法、基于神经网络的检测算法3类,最后探讨了V2X通信中OTFS信号检测目前所面临的技术挑战与未来的发展趋势。     

基于可见光通信的停车场泊车自动导航系统

为满足车辆定位需求并实现高效率停车场泊车导航,设计基于可见光通信的停车场泊车自动导航系统,系统的车位信息监测模块通过结合泊车位上方LED照明装置和车位地面的光敏电阻形成光电检测装置,获取停车场车位状态信息后,经STM32单片机产生频率差异显著方波信号;可见光信号调制模块依据可见光通信原理,将方波信号经信号调制电路转换成电信号,驱动停车位的LED灯光源,传输光信号至定位导航模块;定位导航模块利用内置摄像头接收可见光定位信号,经信号解析、定位等步骤获取可见光信号来源位置信息,实现停车场泊车自动导航。实验结果表明,该系统可有效满足车辆定位需求,车辆定位误差控制在0.20 m范围内,且泊车导航所需时间较短,具备较高泊车导航效率。     

基于LED的校园照明节能控制系统

设计了一套适用于高校校园的LED照明节能控制系统,包括LED恒流驱动电源、光照度检测模块、红外热释电传感模块、计数模块和GPRS无线通信模块等。配套了18 W T8型LED灯管和50 W面板型两种LED灯具,分别用于替换教室荧光灯和校园道路路灯。驱动电源通用性好,支持12 V和24 V两种直流输入电压,最大输出功率可达150 W,且输出电压自适应,使其能够兼容多种串并方式的LED灯具。该系统同样适用于博物馆、大型酒店、地下停车场等照明需求量大的应用场合。     

蜂窝车联网中基于服务异构性的V2V通信资源分配算法研究

在支持车与车直接通信(V2V)的蜂窝网络场景下,针对密集环境下复用车与设备(V2I)上行链路的资源分配问题,在V2V的干扰下,利用移动链路的信道状态信息(CSI)的慢衰落统计,联合通信可靠性、功率控制,建立最大化V2I信道容量的优化模型以满足车辆网络服务的异构性的需求。基于此,该文提出一种基于超图理论和遗传算法的资源分配算法。仿真结果表明,该算法在保证V2V通信可靠性的前提下,提高了V2I的信道容量。     

车联网环境下基于Cuckoo过滤器的轻量V2I认证算法

基于假名认证机制是保护车与路边设施间通信(V2I)隐私的有效方法,传统的基于证书撤销清单(CRL)方法存在通信和计算开销大的问题。为此,提出基于布谷鸟(Cuckoo)过滤器的轻量V2I认证算法(CFLA)。CFLA算法通过局部信任中心(LTA)给其覆盖内的车辆分配假名,并利用Merkle散列树(MHT)存储车辆假名,每辆车维持一棵独立MHT。同时,采用布谷鸟过滤器(CF)数据结构,降低存储、计算和通信开销。安全性能分析表明,提出的CFLA算法能够具有防御中间攻击、重放攻击的能力。相比于相关的同类算法,CFLA算法降低了认证开销。     

Multi-layer neural network algorithm for vehicle-to-everything communication in 5G networks

Internet of Vehicles (IoVs), the emerging trend of Internet of Things (IoTs), has undoubtedly become a promising trend to improve communication among vehicles on the roads. Vehicle-to-everything (V2X) communication that is based on 5G technology enables vehicle users to communicate and collaborate with each other to enhance road traffic efficiency and safety. Owing to the increased traffic load and restricted resources of existing network substructure, a channel that responds to the latency and reliability needs of V2X communication must be designed. Thereby, several intelligent spectrum allocation techniques have been proposed to improve the system's overall effectiveness. In this paper, we discuss the spectrum sharing issue of V2X communication in Device-to-Device (D2D)-based cellular networks. We propose a new multi-layer neural network (MLNN)-based Resource Allocation and sharing approach (MNNRA) for D2D-based V2X communications. According to the main advantages of MLNN, the proposed algorithm takes several profits by improving system performance while reducing computational complexity. Numerical analysis is presented to approve the effectiveness of our proposed solution in terms of network sum rate, packet reception ratio, resource utilization ratio, and time complexity.     

Beamforming and Scalable Image Processing in Vehicle-to-Vehicle Networks

Vehicle to Vehicle (V2V) communication allows vehicles to wirelessly exchange information on the surrounding environment and enables cooperative perception. It helps prevent accidents, increase the safety of the passengers, and improve the traffic flow efficiency. However, these benefits can only come when the vehicles can communicate with each other in a fast and reliable manner. Therefore, we investigated two areas to improve the communication quality of V2V: First, using beamforming to increase the bandwidth of V2V communication by establishing accurate and stable collaborative beam connection between vehicles on the road; second, ensuring scalable transmission to decrease the amount of data to be transmitted, thus reduce the bandwidth requirements needed for collaborative perception of autonomous driving vehicles. Beamforming in V2V communication can be achieved by utilizing image-based and LIDAR’s 3D data-based vehicle detection and tracking. For vehicle detection and tracking simulation, we tested the Single Shot Multibox Detector deep learning-based object detection method that can achieve a mean Average Precision of 0.837 and the Kalman filter for tracking. For scalable transmission, we simulate the effect of varying pixel resolutions as well as different image compression techniques on the file size of data. Results show that without compression, the file size for only transmitting the bounding boxes containing detected object is up to 10 times less than the original file size. Similar results are also observed when the file is compressed by lossless and lossy compression to varying degrees. Based on these findings using existing databases, the impact of these compression methods and methods of effectively combining feature maps on the performance of object detection and tracking models will be further tested in the real-world autonomous driving system.

     

Optimal solution for malicious node detection and prevention using hybrid chaotic particle dragonfly swarm algorithm in VANETs

Vehicular ad hoc networks (VANETs) have the ability to make changes in travelling and driving mode of people and so on, in which vehicle can broadcast and forward the message related to emergency or present road condition. The safety and efficiency of modern transportation system is highly improved using VANETs. However, the vehicular communication performance is weakened with the sudden emergence of distributed denial of service (DDoS) attacks. Among other attacks, DDoS attack is the fastest attack degrading the VANETs performance due to its node mobility nature. Also, the attackers (cyber terrorists, politicians, etc.) have now considered the DDoS attack as a network service degradation weapon. In current trend, there is a quick need for mitigation and prevention of DDoS attacks in the exploration field. To resolve the conflict of privacy preservation, we propose a fast and secure HCPDS based framework for DDoS attack detection and prevention in VANETs. The Road Side Units (RSUs) have used HCPDS algorithm to evaluate the fitness values of all vehicles. This evaluation process is done for effective detection of spoofing and misbehaving nodes by comparing the obtained fitness value with the statistical information (packet factors, RSU zone, and vehicle dynamics) gathered from the vehicles. The credentials of all worst nodes are cancelled to avoid further communication with other vehicles. In HCPDS algorithm, the PSO updation strategy is added to Dragon fly algorithm to improve the search space. In addition, Chaos theory is applied to tune the parameters of proposed HCPDS algorithm. From the experimental results, it proved that the HCPDS based proposed approach can efficiently meet the requirements of security and privacy in VANETs.

     

基于非线性系统的小信号检测算法研究

本文研究了通信数据链系统的微弱小信号增强与接收检测算法。为了提升通信数据链系统中信号的接收性能，在低信噪比条件下，提出基于非线性系统的小信号检测算法。在非线性系统中，适当强度的噪声在一定程度上会增强输出信号的幅值，从而实现在强大的噪声背景环境下对微弱小信号的增强和检测功能。本文着重研究了非线性系统中的双稳态系统，分析了通信数据链系统中的 MSK 信号输入到双稳态系统中，利用 Matlab 仿真出输入非线性系统前后的信号时域频域波形图，进而分析了基于通信数据链系统的小信号增强与检测算法性能。Matlab 仿真结果表明，本文提出的算法可以有效改善低信噪比条件下的通信信号接收性能，在信号处理与接收领域有着重大的应用前景。