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Correction: Fog computing network security based on resources management 更正:基于资源管理的雾计算网络安全
4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-20 DOI: 10.1186/s13638-023-02318-4
Wided Ben Daoud, Salwa Othmen, Monia Hamdi, Radhia Khdhir, Habib Hamam
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引用次数: 0
Optimized design and application research of smart interactive screen for wireless networks based on federated learning 基于联邦学习的无线网络智能交互屏幕优化设计与应用研究
4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-18 DOI: 10.1186/s13638-023-02315-7
Chang Zhao, Linghao Zhang
Abstract The rapid development of infinite networks and information technology has promoted the wide deployment and rapid growth of intelligent interactive devices. However, at the same time, touch interaction technology also faces many challenges such as lack of precision. This study combines federated learning with LayerGesture technology to optimize and design a touch interaction system with higher interaction accuracy and applies it to practice. The analysis results show that with the increase in the number of iterations of the federated model, the accuracy of the human–computer recognition interaction and the amount of information contained in it increases, and the accuracy curve reaches stability at about 2800 times and is at the optimal interaction adaptation. At this point, the loss function also decreases gradually, while the loss factor tends to 0, which verifies the stability of the optimized model. According to the participants’ interaction experience and experimental results, the optimized LayerGesture technique of the federated learning model has an average correctness rate of 90.4% and the lowest average selection time, while the average selection time of LayerGesture in the interaction area at the edge of the screen is 2510 ms and the average correctness rate is 93.60%, which is better than the Shift technique. In addition, the subjective survey results indicated that more participants favored the optimized LayerGesture technique. In summary, this paper’s joint learning algorithm contributes to the recognition effectiveness and efficiency of intelligent interactive systems.
无限网络和信息技术的飞速发展,促进了智能交互设备的广泛部署和快速增长。但与此同时,触摸交互技术也面临着精度不足等诸多挑战。本研究将联邦学习与LayerGesture技术相结合,优化设计了具有更高交互精度的触摸交互系统,并将其应用于实践。分析结果表明,随着联邦模型迭代次数的增加,人机识别交互的精度和所含信息量增加,精度曲线在2800次左右达到稳定,处于最佳交互自适应状态。此时,损失函数也逐渐减小,损失因子趋于0,验证了优化模型的稳定性。根据参与者的交互经验和实验结果,优化后的联邦学习模型LayerGesture技术的平均正确率为90.4%,平均选择时间最低,而LayerGesture在屏幕边缘交互区域的平均选择时间为2510 ms,平均正确率为93.60%,优于Shift技术。此外,主观调查结果显示,更多的参与者喜欢优化的LayerGesture技术。综上所述,本文的联合学习算法有助于提高智能交互系统的识别有效性和效率。
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引用次数: 0
Hybrid traffic security shaping scheme combining TAS and CQSF of time-sensitive networks in smart grid 智能电网时敏网络中结合TAS和CQSF的混合流量安全整形方案
4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-12 DOI: 10.1186/s13638-023-02278-9
Hongting Zhai, Qingrui Zhang, Ruilin Tang, Yantong Zhang, Lili Sun, Qi Zhai, Ruochen Bian, Xinxin He
Abstract The new intelligent factory introduces Time-Sensitive Network into industrial Ethernet to provide real time and deterministic guarantee communication for production system. Since the problem pertaining to data leakage or damage during transmission has increasingly become pronounced, security protection technology has been introduced, but this technology will bring about a delay in user response and a decline in the quality of service. Meanwhile, ensuring the deterministic mixed transmission of time-sensitive and large-bandwidth data traffic supported by the same switching device is a still challenging problem. Therefore, this study proposes a hybrid security scheduling scheme which combines Time-Aware Shaper and cycle specified queuing and forwarding (CSQF). Specifically, the mechanism first adopts various encryption methods for different traffic, and afterward, it reduces its resource occupation by adjusting the sampling period of the time-sensitive traffic. At the same time, it adopts CSQF to schedule the large-bandwidth data traffic, thereby improving the scheduling success rate. According to the experimental results, this scheme enhances network security and network scheduling success rate by up to 51%. The scheduling of mixed traffic in the Time-sensitive Network is realized securely and efficiently.
摘要新型智能工厂在工业以太网中引入时敏网络,为生产系统提供实时、确定性的通信保障。由于数据在传输过程中泄露或损坏的问题日益突出,引入了安全保护技术,但这种技术会带来用户响应的延迟和服务质量的下降。同时,如何保证同一交换设备支持的时间敏感和大带宽数据流量的确定性混合传输仍然是一个具有挑战性的问题。因此,本研究提出了一种结合时间感知Shaper和周期指定排队转发(CSQF)的混合安全调度方案。该机制首先对不同的流量采用不同的加密方式,然后通过调整对时间敏感的流量的采样周期来减少对资源的占用。同时,采用CSQF对大带宽数据流量进行调度,提高了调度成功率。实验结果表明,该方案将网络安全性和网络调度成功率提高了51%。实现了时间敏感网络中混合流量的安全高效调度。
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引用次数: 0
Physical layer security analysis of IRS-based downlink and uplink NOMA networks 基于irs的下行和上行NOMA网络物理层安全性分析
4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-11 DOI: 10.1186/s13638-023-02309-5
Si-Phu Le, Hong-Nhu Nguyen, Nhat-Tien Nguyen, Cuu Ho Van, Anh-Tu Le, Miroslav Voznak
Abstract In recent years, the development of intelligent reflecting surface (IRS) in wireless communications has enabled control of radio waves to reduce the detrimental impacts of natural wireless propagation. These can achieve significant spectrum and energy efficiency in wireless networks. Non-orthogonal multiple access (NOMA) technology, on the other hand, is predicted to improve the spectrum efficiency of fifth-generation and later wireless networks. Motivated by this reality, we consider the IRS-based NOMA network in the downlink and uplink scenario with a pernicious eavesdropper. Moreover, we investigated the physical layer security (PLS) of the proposed system by invoking the connection outage probability (COP), secrecy outage probability (SOP), and average secrecy rate (ASR) with analytical derivations. The simulation results reveal that (i) it is carried out to validate the analytical formulas, (ii) the number of meta-surfaces in IRS, transmit power at the base station, and power allocation parameters all play an essential role in improving the system performance, and (iii) it demonstrates the superiority of NOMA to the traditional orthogonal multiple access (OMA).
近年来,智能反射面(IRS)技术在无线通信领域的发展,使无线电波的控制能够减少自然无线传播的不利影响。这些可以在无线网络中实现显著的频谱和能源效率。另一方面,预计非正交多址(NOMA)技术将提高第五代及以后无线网络的频谱效率。基于这一现实,我们考虑了具有恶意窃听者的基于irs的NOMA网络下行和上行场景。此外,我们还通过调用连接中断概率(COP)、保密中断概率(SOP)和平均保密率(ASR)对所提出系统的物理层安全性(PLS)进行了分析推导。仿真结果表明:(1)分析公式的正确性得到了验证;(2)IRS元曲面数、基站发射功率和功率分配参数对系统性能的提高都起着至关重要的作用;(3)NOMA相对于传统的正交多址(OMA)具有优越性。
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引用次数: 0
Design and development of multiband PIFA antenna for vehicular LTE/5G and V2X communication 用于车载LTE/5G和V2X通信的多频段PIFA天线的设计与开发
4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-10 DOI: 10.1186/s13638-023-02306-8
Amruta A. Nikam, Rupali B. Patil
Abstract This paper aims to introduce a custom-designed multiband planar inverted-F antenna (PIFA) suitable for automotive applications in LTE/5G schemes operating under 6 GHz, as well as Vehicle-to-Everything (V2X) communications. The PIFA antenna has a broad bandwidth capability, resonating from 950 MHz to 6 GHz. The proposed PIFA antenna is divided into three parts: the top, front, and back, resulting in a unique and effective antenna structure. The antenna is fabricated using a substrate made of FR4 material with a dielectric constant of 4.4. The whole measurements of the antenna are 54 × 38 × 25 mm 3 .The proposed PIFA antenna has been tested and has achieved a voltage standing wave ratio (VSWR) of less than 2 across the entire frequency range of 950 MHz to 6 GHz. Additionally, the maximum gain achieved by the antenna is 7.08 dBi at a frequency of 5.5 GHz, 6.81 dBi at 5.2 GHz, and 6.65 dBi at 5.9 GHz. The antenna also achieved a gain of 6.67 dBi at 3.8 GHz and a gain of 3.31 dBi at 1.7 GHz. Overall, this paper presents a well-designed and effective multiband PIFA antenna that is appropriate for use in vehicular applications. The antenna ability to cover a wide range of bandwidth and achieve high gain makes it an excellent candidate for use in LTE/5G systems and V2X communications.
摘要:本文旨在介绍一种定制设计的多频段平面倒f天线(PIFA),适用于6 GHz以下LTE/5G方案的汽车应用,以及车联网(V2X)通信。PIFA天线具有宽带宽能力,谐振范围从950 MHz到6 GHz。所提出的PIFA天线分为上、前、后三部分,形成独特有效的天线结构。该天线采用介电常数为4.4的FR4材料制成的衬底制造。该天线的总测量值为54 × 38 × 25 mm。该天线已经过测试,并在950 MHz至6 GHz的整个频率范围内实现了小于2的电压驻波比(VSWR)。此外,该天线在5.5 GHz频率下的最大增益为7.08 dBi,在5.2 GHz频率下为6.81 dBi,在5.9 GHz频率下为6.65 dBi。该天线在3.8 GHz时的增益为6.67 dBi,在1.7 GHz时的增益为3.31 dBi。总体而言,本文提出了一种设计良好且有效的多频段PIFA天线,适用于车载应用。天线覆盖大范围带宽并实现高增益的能力使其成为LTE/5G系统和V2X通信中使用的优秀候选者。
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引用次数: 0
RIS-enabled smart wireless environments: deployment scenarios, network architecture, bandwidth and area of influence 支持ris的智能无线环境:部署场景、网络架构、带宽和影响范围
4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-10 DOI: 10.1186/s13638-023-02295-8
George C. Alexandropoulos, Dinh-Thuy Phan-Huy, Konstantinos D. Katsanos, Maurizio Crozzoli, Henk Wymeersch, Petar Popovski, Philippe Ratajczak, Yohann Bénédic, Marie-Helene Hamon, Sebastien Herraiz Gonzalez, Placido Mursia, Marco Rossanese, Vincenzo Sciancalepore, Jean-Baptiste Gros, Sergio Terranova, Gabriele Gradoni, Paolo Di Lorenzo, Moustafa Rahal, Benoit Denis, Raffaele D’Errico, Antonio Clemente, Emilio Calvanese Strinati
Abstract Reconfigurable intelligent surfaces (RISs) constitute the key enabler for programmable electromagnetic propagation environments and are lately being considered as a candidate physical-layer technology for the demanding connectivity, reliability, localisation, and sustainability requirements of next-generation wireless networks. In this paper, we first present the deployment scenarios for RIS-enabled smart wireless environments that have been recently designed within the ongoing European Union Horizon 2020 RISE-6G project, as well as a network architecture integrating RISs with existing standardised interfaces. We identify various RIS deployment strategies and sketch the core architectural requirements in terms of RIS control and signalling, depending on the RIS hardware architectures and respective capabilities. Furthermore, we introduce and discuss, with the aid of simulations and reflect array measurements, two novel metrics that emerge in the context of RIS-empowered wireless systems: the RIS bandwidth of influence and the RIS area of influence. Their extensive investigation corroborates the need for careful deployment and planning of the RIS technology in future wireless networks.
可重构智能表面(RISs)构成了可编程电磁传播环境的关键促成因素,最近被认为是下一代无线网络苛刻的连接性、可靠性、本地化和可持续性要求的候选物理层技术。在本文中,我们首先介绍了最近在正在进行的欧盟地平线2020 RISE-6G项目中设计的支持RISs的智能无线环境的部署场景,以及将RISs与现有标准化接口集成在一起的网络架构。根据RIS硬件架构和各自的能力,我们确定了各种RIS部署策略,并概述了RIS控制和信号方面的核心架构要求。此外,借助模拟和反射阵列测量,我们介绍并讨论了在RIS授权无线系统背景下出现的两个新指标:RIS影响带宽和RIS影响区域。他们的广泛调查证实了在未来无线网络中仔细部署和规划RIS技术的必要性。
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引用次数: 7
Auto scheduling through distributed reinforcement learning in SDN based IoT environment 基于SDN的物联网环境下通过分布式强化学习实现自动调度
4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-09 DOI: 10.1186/s13638-023-02314-8
Yuanyuan Wu
Abstract The Internet of Things (IoT), which is built on software-defined networking (SDN), employs a paradigm known as channel reassignment. This paradigm has great potential for enhancing the communication capabilities of the network. The traffic loads may be scheduled more effectively with the help of an SDN controller, which allows for the transaction of matching channels via a single connection. The present techniques of channel reassignment, on the other hand, are plagued by problems with optimisation and cooperative multi-channel reassignment, which affect both traffic and routers. In this paper, we provide a framework for SDN–IoT in the cloud that permits multi-channel reassignment and traffic management simultaneously. The multi-channel reassignment based on traffic management is optimised via the use of a deep reinforcement learning technique, which was developed in this paper. We do an analysis of the performance metrics in order to optimise the throughput while simultaneously reducing the rate of packet loss and the amount of delay in the process. This is achieved by distributing the required traffic loads over the linked channels that make up a single connection.
建立在软件定义网络(SDN)基础上的物联网(IoT)采用了一种称为信道重新分配的范式。这种模式在增强网络通信能力方面具有巨大的潜力。在SDN控制器的帮助下,可以更有效地调度流量负载,SDN控制器允许通过单个连接处理匹配通道。另一方面,现有的信道重分配技术存在着优化和协同多信道重分配的问题,这些问题对流量和路由器都有影响。在本文中,我们为云中的SDN-IoT提供了一个框架,该框架允许同时进行多通道重新分配和流量管理。基于交通管理的多通道重新分配通过使用深度强化学习技术进行优化,该技术在本文中开发。我们对性能指标进行了分析,以优化吞吐量,同时减少丢包率和过程中的延迟量。这是通过在组成单个连接的链接通道上分配所需的流量负载来实现的。
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引用次数: 0
Enhancing throughput using channel access priorities in frequency hopping network using federated learning 利用联邦学习在跳频网络中利用信道访问优先级提高吞吐量
4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-05 DOI: 10.1186/s13638-023-02313-9
Yu Han, Xiaowei Zhu
Abstract The data are sent by the nodes taking part in frequency hopping communications (FHC) utilising carrier frequencies and time slots that are pseudo-randomly assigned. Because of this, a high degree of protection against eavesdropping and anti-interference capabilities is provided. When using FHC in an environment, sharing time and frequency resources, avoiding collisions, and differentiating services are all made more complex as a result of this. A protocol for FHC that is based on dispersed wireless networks is presented by the authors of this research. It is a mechanism for multiple access control, which is prioritised and distributed. The ratio of empty channels metric can be found in the previous sentence. It is possible to provide priority in channel access by assigning different preset ratios of empty channel thresholds to the various traffic classes. Frames from frequency spread segments that have a partial collision are included as well. An analytical model is simulated for the analysis in terms of collision probability, transmission probability, and frame service time in order to carry out a theoretical examination of the performance of FHC. The objective of this inquiry is to determine how well FHC works. The analytical model has been proven correct by the exhaustive simulations as well as the theoretical findings. Cloud platforms are often used in the instruction of the most cutting-edge machine learning techniques of today, such as deep neural networks. This is done in order to take advantage of the cloud's capacity to scale elastically. In order to satisfy the criteria of these sorts of applications, federated learning, has been proposed as a distributed machine learning solution. This is done in order to fulfil the requirements of these kinds of applications. In federated learning (FL), even though everyone who uses the system works together to train a model, nobody ever shares their data with anybody else. Each user trains a local model with their own data, and then communicates the updated models with a FL server so that the data can be aggregated and a global model can be constructed. This process ensures that each user's model is unique. This process is repeated until a global model has been developed. This kind of training not only reduces the amount of network overhead that is necessary to transfer data to a centralised server, but it also safeguards the personal information of the users. Within the framework of this work, we looked at the feasibility of using the FL technique of learning on the many devices that are part of the dispersed network. On a centralised server, we conduct an analysis of the performance of the FL model by comparing its accuracy and the amount of time it takes to train using a range of various parameter value combinations. Additionally, the accuracy of these federated models may be made to reach a level that is comparable to that of the accuracy of central models.
跳频通信(FHC)中的节点利用伪随机分配的载波频率和时隙发送数据。因此,提供了高度的防窃听和抗干扰能力。当在一个环境中使用FHC时,共享时间和频率资源、避免冲突、区分业务都变得更加复杂。本文提出了一种基于分散无线网络的FHC协议。它是一种多访问控制机制,具有优先级和分布式。空通道的比例度量可以在前面的句子中找到。通过为各种业务分类分配不同的空信道阈值的预设比率,可以在信道访问中提供优先级。有部分碰撞的频率扩展段的帧也包括在内。为了对FHC的性能进行理论检验,从碰撞概率、传输概率和帧服务时间三个方面对分析模型进行了仿真。本次调查的目的是确定FHC的效果如何。详尽的模拟和理论研究结果证明了分析模型的正确性。云平台经常用于当今最尖端的机器学习技术的指导,例如深度神经网络。这样做是为了利用云的弹性扩展能力。为了满足这些应用的标准,联邦学习作为一种分布式机器学习解决方案被提出。这样做是为了满足这类应用程序的需求。在联邦学习(FL)中,即使每个使用系统的人都一起训练模型,也没有人与其他人分享他们的数据。每个用户用自己的数据训练一个本地模型,然后将更新后的模型与FL服务器通信,以便可以聚合数据并构建全局模型。这个过程确保每个用户的模型是唯一的。这一过程不断重复,直到形成一个全球模型。这种培训不仅减少了将数据传输到中央服务器所需的网络开销,而且还保护了用户的个人信息。在这项工作的框架内,我们研究了在分散网络的许多设备上使用FL学习技术的可行性。在集中式服务器上,我们通过比较其准确性和使用一系列不同参数值组合进行训练所需的时间来分析FL模型的性能。此外,可以使这些联邦模型的精度达到与中心模型的精度相当的水平。
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引用次数: 0
Blockchain managed federated learning for a secure IoT framework 区块链为安全的物联网框架管理联邦学习
4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-02 DOI: 10.1186/s13638-023-02311-x
Jiayong Chai, Jian Li, Muhua Wei, Chuangying Zhu
Abstract In this work, we present a blockchain-based federated learning (FL) framework that aims achieving high system efficiency while simultaneously addressing issues relating to data sparsity and the disclosure of private information. It is more efficient to build a number of smaller clusters rather than one big cluster for multiple networks. Blockchain-based FL is carried out in each cluster, with the model changes being compiled at the end of the process. Following that, the accumulated updates are swapped across the clusters, which, in practise, improves the updates that are accessible for each cluster. When compared to the extensive interactions that take place in blockchain-based FL, cluster-based FL only sends a limited number of aggregated updates across a substantial distance. This is in contrast to the extensive interactions that take place in blockchain-based FL. In order to conduct an analysis of our system, we have implemented the prototypes of both cluster and blockchain-based FL models. The findings of the experiments show that cluster-based FL model raise the accuracy goes upto 72.6%, and goes down to 11%. The loss goes upto 3.6 and goes down to 0.8. In addition, cluster-based FL model has the potential to hasten the convergence of the model, provided that the same quantity of data is input into it. The reason for this is due to the fact that during a training cycle, cluster-based FL model combines the computational resources of many different clusters.
在这项工作中,我们提出了一个基于区块链的联邦学习(FL)框架,旨在实现高系统效率,同时解决与数据稀疏性和私有信息披露相关的问题。为多个网络构建许多较小的集群比构建一个大集群更有效。基于区块链的FL在每个集群中进行,在流程结束时编译模型更改。然后,在集群之间交换累积的更新,这实际上改善了每个集群可访问的更新。与基于区块链的FL中发生的广泛交互相比,基于集群的FL仅在相当长的距离内发送有限数量的聚合更新。这与基于区块链的FL中发生的广泛交互形成鲜明对比。为了对我们的系统进行分析,我们实现了集群和基于区块链的FL模型的原型。实验结果表明,基于聚类的FL模型将准确率提高到72.6%,将准确率降低到11%。损失上升到3.6,下降到0.8。此外,在输入相同数量的数据的情况下,基于聚类的FL模型具有加速模型收敛的潜力。这是因为在一个训练周期中,基于聚类的FL模型结合了许多不同聚类的计算资源。
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引用次数: 0
Switching mode allocation in planning paths for vehicular network communication 车联网通信规划路径中的交换模式分配
4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-02 DOI: 10.1186/s13638-023-02312-w
Dingzhu Lu
Abstract Because of the increased mobility of vehicle users, it might be difficult to keep communication services in vehicle networks effective and dependable. Huge hurdles have been presented to vehicular networks as a result of the meteoric rise in the amount of data, which comes with the needs of high dependability and low latency. The deployment of access point servers at geographic locations that are closer to the vehicles in order to provide real-time service to applications that are based on the vehicles is one possible option. However, there is a limited amount of cache store space, and there is also a lack of a tractable access mode allocation method. As a result of these factors, it is very difficult to strike a compromise between the network transmission performance and fronthaul savings. Because the signal-to-interference-ratio (SIR) can be enhanced with switching mode in vehicular infrastructure, it may be possible to achieve higher levels of dependability. To serve all of the vehicles, the conventional allocation in vehicular network may not be sufficient on its own for two reasons: (1) the number of vehicles exceeds the number of paths, and (2) a vehicle may be located outside of the coverage path. Therefore, the implementation of switching mode allocation in vehicular communication is very necessary in order to increase the number of vehicles that can be supplied. In this paper, allocation using V2I, V2V, and V2X modes have been analyzed to provide dependable coverage for vehicles. These methods are used for communicating with other vehicles. In this paper, the numerical analysis has been performed such that SIR is optimized. In switching mode allocation, it has been shown that establishing a variable SIR threshold is helpful in achieving a path coverage that can be relied upon. It has been shown beyond a reasonable doubt that the coverage probability is likewise directly dependent on SIR thresholds. The theoretical analysis is verified, and it is confirmed that the suggested method is capable of achieving significant performance improvement in terms of coverage probability and data rate.
摘要随着车辆用户移动性的增加,车辆网络中通信服务的有效性和可靠性难以保证。由于数据量的迅速增长,伴随着高可靠性和低延迟的需求,车载网络面临着巨大的障碍。在离车辆更近的地理位置部署接入点服务器,以便为基于车辆的应用程序提供实时服务是一种可能的选择。但是,缓存存储空间有限,而且还缺乏可处理的访问模式分配方法。由于这些因素,很难在网络传输性能和前传节省之间达成妥协。由于信号干扰比(SIR)可以通过车辆基础设施的切换模式得到增强,因此有可能实现更高水平的可靠性。为了满足所有车辆的需求,车辆网络中传统的分配方式本身可能不够,原因有二:(1)车辆数量超过路径数量,(2)车辆可能位于覆盖路径之外。因此,为了增加可提供的车辆数量,在车辆通信中实现切换模式分配是非常必要的。本文分析了V2I, V2V和V2X模式的分配,为车辆提供可靠的覆盖。这些方法用于与其他车辆通信。本文进行了数值分析,对SIR进行了优化。在交换模式分配中,已经证明建立可变SIR阈值有助于实现可依赖的路径覆盖。毫无疑问,覆盖概率同样直接依赖于SIR阈值。对理论分析进行了验证,证实了所提出的方法在覆盖概率和数据速率方面能够取得显著的性能提升。
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引用次数: 0
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EURASIP Journal on Wireless Communications and Networking
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