384 research outputs found

    Socially Aware V2X Localized QoS

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    Vehicle-to-everything (V2X) is a core 5G technology. V2X and its enabler, Device-to-Device (D2D), are essential for the Internet of Things (IoT) and the Internet of Vehicles (IoV). V2X enables vehicles to communicate with other vehicles (V2V), networks (V2N), and infrastructure (V2I). While V2X enables ubiquitous vehicular connectivity, the impact of bursty data on the network's overall Quality of Service (QoS), such as when a vehicle accident occurs, is often ignored. In this work, we study both 4G and 5G V2X utilizing Evolved Universal Terrestrial Radio Access New Radio (E-UTRA-NR) and propose the use of socially aware 5G NR Dual Connectivity (en-DC) for traffic differentiation. We also propose localized QoS, wherein high-priority QoS flows traverse 5G road side units (RSUs) and normal-priority QoS flows traverse 4G Base Station (BS). We formulate a max-min fair QoS-aware Non-Orthogonal Multiple Access (NOMA) resource allocation scheme, QoS reclassify. QoS reclassify enables localized QoS and traffic steering to mitigate bursty network traffic's impact on the network's overall QoS. We then solve QoS reclassify via Integer Linear Programming (ILP) and derive its approximation. We demonstrate that both optimal and approximation QoS reclassify resource allocation schemes in our socially aware QoS management methodology outperform socially unaware legacy 4G V2X algorithms (no localized QoS support, no traffic steering) and socially aware 5G V2X (no localized QoS support, yet utilizes traffic steering). Our proposed QoS reclassify scheme's QoS flow end-to-end latency requires only โ‰ˆย 15%\approx~15\% of the time legacy 4G V2X requires.Comment: This work has been submitted to IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Under review by IEEE Internet of Things journa

    Five Facets of 6G: Research Challenges and Opportunities

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    Whilst the fifth-generation (5G) systems are being rolled out across the globe, researchers have turned their attention to the exploration of radical next-generation solutions. At this early evolutionary stage we survey five main research facets of this field, namely {\em Facet~1: next-generation architectures, spectrum and services, Facet~2: next-generation networking, Facet~3: Internet of Things (IoT), Facet~4: wireless positioning and sensing, as well as Facet~5: applications of deep learning in 6G networks.} In this paper, we have provided a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, applications as well as designs. We have portrayed a plethora of heterogeneous architectures relying on cooperative hybrid networks supported by diverse access and transmission mechanisms. The vulnerabilities of these techniques are also addressed and carefully considered for highlighting the most of promising future research directions. Additionally, we have listed a rich suite of learning-driven optimization techniques. We conclude by observing the evolutionary paradigm-shift that has taken place from pure single-component bandwidth-efficiency, power-efficiency or delay-optimization towards multi-component designs, as exemplified by the twin-component ultra-reliable low-latency mode of the 5G system. We advocate a further evolutionary step towards multi-component Pareto optimization, which requires the exploration of the entire Pareto front of all optiomal solutions, where none of the components of the objective function may be improved without degrading at least one of the other components

    On the needs and requirements arising from connected and automated driving

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    Future 5G systems have set a goal to support mission-critical Vehicle-to-Everything (V2X) communications and they contribute to an important step towards connected and automated driving. To achieve this goal, the communication technologies should be designed based on a solid understanding of the new V2X applications and the related requirements and challenges. In this regard, we provide a description of the main V2X application categories and their representative use cases selected based on an analysis of the future needs of cooperative and automated driving. We also present a methodology on how to derive the network related requirements from the automotive specific requirements. The methodology can be used to analyze the key requirements of both existing and future V2X use cases

    ์ž์œจ์ฃผํ–‰์„ ์œ„ํ•œ V2X ๊ธฐ๋ฐ˜ ์ฐจ๋Ÿ‰ CDN ์„ค๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณตํ•™์ „๋ฌธ๋Œ€ํ•™์› ์‘์šฉ๊ณตํ•™๊ณผ, 2021. 2. ๊น€์„ฑ์šฐ.Recent technical innovation has driven the evolution of autonomous vehicles. To improve safety as well as on-road vehicular experience, vehicles should be connected with each other or to vehicular networks. Some specification groups, e.g., IEEE and 3GPP, have studied and released vehicular communication requirements and architecture. IEEEs Wireless Access in Vehicular Environment focuses on dedicated and short-range communication, while 3GPPs New radio V2X supports not only sidelink but also uplink communication. The 3GPP Release 16, which supports 5G New Radio, offers evolved functionalities such as network slice, Network Function Virtualization, and Software-Defined Networking. In this study, we define and design a vehicular network architecture compliant with 5G core networks. For localization of autonomous driving vehicles, a high-definition map needs to contain the context of trajectory . We also propose new methods by which autonomous vehicles can push and pull map content efficiently, without causing bottlenecks on the network core. We evaluate the performance of V2X and of the proposed caching policy via network simulations. Experimental results indicate that the proposed method improves the performance of vehicular content delivery in real-world road environments.์ตœ๊ทผ๋“ค์–ด ๊ธฐ์ˆ ์˜ ํ˜์‹ ์€ ์ž์œจ์ฃผํ–‰ ์ž๋™์ฐจ์˜ ๋ฐœ์ „์„ ๊ฐ€์†ํ™” ํ•˜๊ณ  ์žˆ๋‹ค. ๋ณด๋‹ค ๋†’์€ ์ˆ˜์ค€์˜ ์ž์œจ ์ฃผํ–‰์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•ด์„œ, ์ฐจ๋Ÿ‰์€ ๋„คํŠธ์›Œํฌ๋ฅผ ํ†ตํ•ด ์„œ๋กœ ์—ฐ๊ฒฐ๋˜์–ด ์žˆ์–ด์•ผ ํ•˜๊ณ  ์ฐจ๋Ÿ‰์˜ ์•ˆ์ „๊ณผ ํŽธ์˜์„ฑ์„ ํ–ฅ์ƒ ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋„๋ก ์ •๋ณด๋ฅผ ๊ณต์œ  ํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ํ‘œ์ค€ํ™” ๋‹จ์ฒด์ธ IEEE์™€ 3GPP๋Š” ์ฐจ๋Ÿ‰ ํ†ต์‹  ์š”๊ตฌ์‚ฌํ•ญ, ์•„ํ‚คํ…์ฒ˜๋ฅผ ์—ฐ๊ตฌํ•˜๊ณ  ๊ฐœ์ •ํ•ด์™”๋‹ค. IEEE๊ฐ€ ์ „์šฉ ์ฑ„๋„์„ ํ†ตํ•œ ๊ทผ์ ‘ ์ง€์—ญ ํ†ต์‹ ์— ์ดˆ์ ์„ ๋งž์ถ”๋Š” ๋ฐ˜๋ฉด์—, 3GPP์˜ New Radio V2X๋Š” Sidelink ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ Uplink ํ†ต์‹ ์„ ๋™์‹œ์— ์ง€์›ํ•œ๋‹ค. 5G ํ†ต์‹ ์„ ์ง€์›ํ•˜๋Š” 3GPP Release 16์€ Network Slice, NFV, SDN๊ณผ ๊ฐ™์€ ์ƒˆ๋กœ์šด ํ†ต์‹  ๊ธฐ๋Šฅ๋“ค์„ ์ œ๊ณตํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ์ƒˆ๋กญ๊ฒŒ ์ •์˜๋œ 5G Core Network Architecture๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ฐจ๋Ÿ‰ ๋„คํŠธ์›Œํฌ๋ฅผ ์ •์˜ํ•˜๊ณ  ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ž์œจ์ฃผํ–‰ ์ž๋™์ฐจ์˜ ์ธก์œ„๋ฅผ ์œ„ํ•ด์„œ, ๊ณ ํ•ด์ƒ๋„ ์ง€๋„๋Š” ๊ฐ ๊ตฌ์„ฑ์š”์†Œ๋“ค์˜ ์˜๋ฏธ์™€ ์†์„ฑ์„ ์ž์„ธํ•˜๊ฒŒ ํฌํ•จํ•˜๊ณ  ์žˆ์–ด์•ผ ํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ์ด ์—ฐ๊ตฌ์—์„œ V2X ๋„คํŠธ์›Œํฌ ์ƒ์— HD map์„ ์ค‘๊ณ„ํ•  ์ˆ˜ ์žˆ๋Š” Edge Server๋ฅผ ์ œ์•ˆ ํ•จ์œผ๋กœ์จ, ์ค‘์•™์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๋ณ‘๋ชฉํ˜„์ƒ์„ ์ค„์ด๊ณ  ์ „์†ก Delay๋ฅผ ์ตœ์†Œํ™”ํ•œ๋‹ค. ๋˜ํ•œ Edge์˜ ์ปจํ…์ธ ๋ฅผ ๋“ฑ๋กํ•˜๊ณ  ์‚ญ์ œํ•˜๋Š” ์ •์ฑ…์œผ๋กœ ๊ธฐ์กด์˜ LRU, LFU๊ฐ€ ์•„๋‹Œ ์ƒˆ๋กœ์šด ์ปจํ…์ธ  ๊ต์ฒด ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์‹ค์ œ ์ฃผํ–‰ ์‹œํ—˜๊ณผ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•œ ์‹คํ—˜์„ ํ†ตํ•ด ์ „์†ก ํ’ˆ์งˆ์„ ํ–ฅ์ƒ์‹œ์ผฐ์œผ๋ฉฐ, Edge ์ปจํ…์ธ ์˜ ํ™œ์šฉ๋„๋ฅผ ๋†’์˜€๋‹ค.I. Introduction 1 II. Related Works 6 2.1 V2X Standardization 6 2.1.1 IEEE WAVE 6 2.1.2 3GPP C-V2X 9 2.2 Geographic Contents 14 2.3 Vehicular Content Centric Network 17 III. System Modeling 20 3.1 NR-V2X Architecture Analysis 20 3.2 Caching Strategy for HD Map Acquisition 23 IV. Evaluation 30 4.1 Contents Replacement Strategy 30 4.2 V2X Characteristics 36 4.3 Edge Performance in Driving on the Road 38 4.4 Edge Performance on 3D Point Clouds Caching for Localization 44 V. Conclusion 47 Bibliography 49 Abstract 54Maste

    LiDAR aided simulation pipeline for wireless communication in vehicular traffic scenarios

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    Abstract. Integrated Sensing and Communication (ISAC) is a modern technology under development for Sixth Generation (6G) systems. This thesis focuses on creating a simulation pipeline for dynamic vehicular traffic scenarios and a novel approach to reducing wireless communication overhead with a Light Detection and Ranging (LiDAR) based system. The simulation pipeline can be used to generate data sets for numerous problems. Additionally, the developed error model for vehicle detection algorithms can be used to identify LiDAR performance with respect to different parameters like LiDAR height, range, and laser point density. LiDAR behavior on traffic environment is provided as part of the results in this study. A periodic beam index map is developed by capturing antenna azimuth and elevation angles, which denote maximum Reference Signal Receive Power (RSRP) for a simulated receiver grid on the road and classifying areas using Support Vector Machine (SVM) algorithm to reduce the number of Synchronization Signal Blocks (SSBs) that are needed to be sent in Vehicle to Infrastructure (V2I) communication. This approach effectively reduces the wireless communication overhead in V2I communication

    Vehicular Fog Computing Enabled Real-time Collision Warning via Trajectory Calibration

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    Vehicular fog computing (VFC) has been envisioned as a promising paradigm for enabling a variety of emerging intelligent transportation systems (ITS). However, due to inevitable as well as non-negligible issues in wireless communication, including transmission latency and packet loss, it is still challenging in implementing safety-critical applications, such as real-time collision warning in vehicular networks. In this paper, we present a vehicular fog computing architecture, aiming at supporting effective and real-time collision warning by offloading computation and communication overheads to distributed fog nodes. With the system architecture, we further propose a trajectory calibration based collision warning (TCCW) algorithm along with tailored communication protocols. Specifically, an application-layer vehicular-to-infrastructure (V2I) communication delay is fitted by the Stable distribution with real-world field testing data. Then, a packet loss detection mechanism is designed. Finally, TCCW calibrates real-time vehicle trajectories based on received vehicle status including GPS coordinates, velocity, acceleration, heading direction, as well as the estimation of communication delay and the detection of packet loss. For performance evaluation, we build the simulation model and implement conventional solutions including cloud-based warning and fog-based warning without calibration for comparison. Real-vehicle trajectories are extracted as the input, and the simulation results demonstrate that the effectiveness of TCCW in terms of the highest precision and recall in a wide range of scenarios
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