384 research outputs found
Socially Aware V2X Localized QoS
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
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
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
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
์์จ์ฃผํ์ ์ํ V2X ๊ธฐ๋ฐ ์ฐจ๋ CDN ์ค๊ณ
ํ์๋
ผ๋ฌธ (์์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณตํ์ ๋ฌธ๋ํ์ ์์ฉ๊ณตํ๊ณผ, 2021. 2. ๊น์ฑ์ฐ.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.์ต๊ทผ๋ค์ด ๊ธฐ์ ์ ํ์ ์ ์์จ์ฃผํ ์๋์ฐจ์ ๋ฐ์ ์ ๊ฐ์ํ ํ๊ณ ์๋ค. ๋ณด๋ค ๋์ ์์ค์ ์์จ ์ฃผํ์ ๊ตฌํํ๊ธฐ ์ํด์, ์ฐจ๋์ ๋คํธ์ํฌ๋ฅผ ํตํด ์๋ก ์ฐ๊ฒฐ๋์ด ์์ด์ผ ํ๊ณ ์ฐจ๋์ ์์ ๊ณผ ํธ์์ฑ์ ํฅ์ ์ํฌ ์ ์๋๋ก ์ ๋ณด๋ฅผ ๊ณต์ ํ ์ ์์ด์ผ ํ๋ค. ํ์คํ ๋จ์ฒด์ธ IEEE์ 3GPP๋ ์ฐจ๋ ํต์ ์๊ตฌ์ฌํญ, ์ํคํ
์ฒ๋ฅผ ์ฐ๊ตฌํ๊ณ ๊ฐ์ ํด์๋ค. IEEE๊ฐ ์ ์ฉ ์ฑ๋์ ํตํ ๊ทผ์ ์ง์ญ ํต์ ์ ์ด์ ์ ๋ง์ถ๋ ๋ฐ๋ฉด์, 3GPP์ New Radio V2X๋ Sidelink ๋ฟ๋ง ์๋๋ผ Uplink ํต์ ์ ๋์์ ์ง์ํ๋ค. 5G ํต์ ์ ์ง์ํ๋ 3GPP Release 16์ Network Slice, NFV, SDN๊ณผ ๊ฐ์ ์๋ก์ด ํต์ ๊ธฐ๋ฅ๋ค์ ์ ๊ณตํ๋ค. ์ด ์ฐ๊ตฌ์์๋ ์๋กญ๊ฒ ์ ์๋ 5G Core Network Architecture๋ฅผ ๋ฐํ์ผ๋ก ์ฐจ๋ ๋คํธ์ํฌ๋ฅผ ์ ์ํ๊ณ ์ค๊ณํ์๋ค. ์์จ์ฃผํ ์๋์ฐจ์ ์ธก์๋ฅผ ์ํด์, ๊ณ ํด์๋ ์ง๋๋ ๊ฐ ๊ตฌ์ฑ์์๋ค์ ์๋ฏธ์ ์์ฑ์ ์์ธํ๊ฒ ํฌํจํ๊ณ ์์ด์ผ ํ๋ค. ์ฐ๋ฆฌ๋ ์ด ์ฐ๊ตฌ์์ V2X ๋คํธ์ํฌ ์์ HD map์ ์ค๊ณํ ์ ์๋ Edge Server๋ฅผ ์ ์ ํจ์ผ๋ก์จ, ์ค์์์ ๋ฐ์ํ ์ ์๋ ๋ณ๋ชฉํ์์ ์ค์ด๊ณ ์ ์ก Delay๋ฅผ ์ต์ํํ๋ค. ๋ํ Edge์ ์ปจํ
์ธ ๋ฅผ ๋ฑ๋กํ๊ณ ์ญ์ ํ๋ ์ ์ฑ
์ผ๋ก ๊ธฐ์กด์ LRU, LFU๊ฐ ์๋ ์๋ก์ด ์ปจํ
์ธ ๊ต์ฒด ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ์๋ค. ์ค์ ์ฃผํ ์ํ๊ณผ ์๋ฎฌ๋ ์ด์
์ ํตํ ์คํ์ ํตํด ์ ์ก ํ์ง์ ํฅ์์์ผฐ์ผ๋ฉฐ, Edge ์ปจํ
์ธ ์ ํ์ฉ๋๋ฅผ ๋์๋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
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
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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