Distributed Estimation Framework for Beyond 5G Intelligent Vehicular Networks

Yuan, Weijie; Li, Shuangyang; Xiang, Lin; Ng, Derrick Wing Kwan

doi:10.1109/ojvt.2020.2989534

Cited by 28 publications

(15 citation statements)

References 134 publications

(145 reference statements)

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“…To figure out a roadmap for the future vision of intelligent services supported by 6G networks, Bariah et al [23] compared enabling technologies of 5G and 6G, and summarized 6G empowered network services with their potential applications in terms of their main challenges and open research issues. Yuan et al [24] presented estimation framework for interest variables in intelligent vehicular networks, in which the factor graph was employed with consensus algorithms to realize estimations in each vehicle and facilitate local processing and communications at RSUs.…”

Section: A Vehicular Network Applicationsmentioning

confidence: 99%

Two-Layer Federated Learning With Heterogeneous Model Aggregation for 6G Supported Internet of Vehicles

Zhou

Liang

She

et al. 2021

IEEE Trans. Veh. Technol.

204

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The vision of the upcoming 6G technologies that have fast data rate, low latency, and ultra-dense network, draws great attentions to the Internet of Vehicles (IoV) and Vehicle-to-Everything (V2X) communication for intelligent transportation systems. There is an urgent need for distributed machine learning techniques that can take advantages of massive interconnected networks with explosive amount of heterogeneous data generated at the network edge. In this study, a two-layer federated learning model is proposed to take advantages of the distributed end-edgecloud architecture typical in 6G environment, and to achieve a more efficient and more accurate learning while ensuring data privacy protection and reducing communication overheads. A novel multi-layer heterogeneous model selection and aggregation scheme is designed as a part of the federated learning process to better utilize the local and global contexts of individual vehicles and road side units (RSUs) in 6G supported vehicular networks. This context-aware distributed learning mechanism is then developed and applied to address intelligent object detection, which is one of the most critical challenges in modern intelligent transportation systems with autonomous vehicles. Evaluation results showed that the proposed method, which demonstrates a higher learning accuracy with better precision, recall and F1 score, outperforms other state-of-the-art methods under 6G network configuration by achieving faster convergence, and scales better with larger numbers of RSUs involved in the learning process. Index Terms-Federated learning, End-edge-cloud computing, Internet of vehicles, Heterogeneous data, 6G technology I. INTRODUCTION EAMLESS and ubiquitous communication infrastructure has been a key enabler of modern intelligent services. While

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Section: A Vehicular Network Applicationsmentioning

confidence: 99%

Two-Layer Federated Learning With Heterogeneous Model Aggregation for 6G Supported Internet of Vehicles

Zhou

Liang

She

et al. 2021

IEEE Trans. Veh. Technol.

204

View full text Add to dashboard Cite

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“…Future technologies will provide smoother upgrade paths that will aid to provide up-to-date and stronger services including security to protect both the intelligent transportation systems and the wireless networks themselves. These advanced technologies will get more sophisticated in future, so will the equipment that allows hackers to exploit their vulnerabilities and making software and security updates possibly easier process in everyone's interest [7].…”

Section: B 5g Wireless Networkmentioning

confidence: 99%

Analysis of VANET Wireless Networking Technologies in Realistic Environments

Tahir

Katz

Rashid

2021

2021 IEEE Radio and Wireless Symposium (RWS)

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Communication is essential to provide an intelligent services to connected cars. For operational services, connected vehicles in VANET (Vehicle Ad hoc Networks) regularly try to transfer large amounts of data for vehicular safety. Similarly, V2x (Vehicle-to-Everything) communication includes vehicles transferring information with each other and with infrastructure i.e., Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) are proven to increase traffic safety and security as well as to enhance the efficiency of intelligent transportation system (ITS). Vehicular connectivity is provided using short-range technologies such as the IEEE 802.110 standard or cellular approaches, such as the 5G network. In this article, we consider combining these technologies in a cooperative manner and aiming at exploiting jointly their advantages. In this cooperative heterogeneous network, the IEEE 802.11p supports safety-related pilot use cases while the provision of non-safetyrelated pilot use cases are supported by the 5G test network.

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“…As such, one can easily estimate the channel by observing the received samples with indices 0 ≤ l ≤ l p + l max and k p − k max ≤ l ≤ k p + k max and comparing them with a preset threshold 3 . Nevertheless, using guard space will inevitably deteriorate the communication efficiency, as the high-mobility 3 The threshold is set to identify whether the received samples are contributed by the pilot. A general value is 3 √ N 0 for additive Gaussian white noise with power spectral density N 0 [31].…”

Section: Uplink Communicationmentioning

confidence: 99%

“…The automotive industry has developed significantly in the past decades to pave the way for the development of digital cities. With the new era of the fifth-generation (5G) mobile networks and beyond, it is expected that the vehicular communication will play an increasingly important role in people's daily lives, as it can support various promising applications, e.g., autonomous driving, traffic management, and on-the-go Internet services in intelligent transportation systems (ITS) [1]- [3]. In addition to the requirement of efficient communication, highly accurate sensing is also required in vehicular networks, which is usually associated with radar systems.…”

Section: Introductionmentioning

confidence: 99%

Integrated Sensing and Communication-assisted Orthogonal Time Frequency Space Transmission for Vehicular Networks

Yuan,

Wei,

et al. 2021

Preprint

Self Cite

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Orthogonal time frequency space (OTFS) modulation is a promising candidate for supporting reliable information transmission in high-mobility vehicular networks. In this paper, we consider the employment of the integrated (radar) sensing and communication (ISAC) technique for assisting OTFS transmission in both uplink and downlink vehicular communication systems. Benefiting from the OTFS-ISAC signals, the roadside unit (RSU) is capable of simultaneously transmitting downlink information to the vehicles and estimating the sensing parameters of vehicles, e.g., locations and speeds, based on the reflected echoes. Then, relying on the estimated kinematic parameters of vehicles, the RSU can construct the topology of the vehicular network that enables the prediction of the vehicle states in the following time instant. Consequently, the RSU can effectively formulate the transmit downlink beamformers according to the predicted parameters to counteract the channel adversity such that the vehicles can directly detect the information without the need of performing channel estimation. As for the uplink transmission, the RSU can infer the delays and Dopplers associated with different channel paths based on the aforementioned dynamic topology of the vehicular network. Thus, inserting guard space as in conventional methods are not needed for uplink channel estimation which removes the required training overhead. Finally, an efficient uplink detector is proposed by taking into account the channel estimation uncertainty. Through numerical simulations, we demonstrate the benefits of the proposed ISAC-assisted OTFS transmission scheme.

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Distributed Estimation Framework for Beyond 5G Intelligent Vehicular Networks

Abstract: The work of D. W. K. Ng was supported in part by the UNSW Digital Grid Futures Institute, UNSW, Sydney, under a cross-disciplinary fund scheme and in part by the Australian Research Council's Discovery Project (DP190101363).

Cited by 28 publications

References 134 publications

Two-Layer Federated Learning With Heterogeneous Model Aggregation for 6G Supported Internet of Vehicles

Two-Layer Federated Learning With Heterogeneous Model Aggregation for 6G Supported Internet of Vehicles

Analysis of VANET Wireless Networking Technologies in Realistic Environments

Integrated Sensing and Communication-assisted Orthogonal Time Frequency Space Transmission for Vehicular Networks

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