Analytical Framework for Mmwave-Enabled V2X Caching

Fatahi-Bafqi, Saeede; Zeinalpour-Yazdi, Zolfa; Asadi, Arash

doi:10.1109/tvt.2020.3047511

Cited by 9 publications

(10 citation statements)

References 35 publications

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“…where P I denotes transmit power at the infrastructure node for RF transmission, h I,N represents channel gain of the RF link, x V denotes unit energy symbol, and n N ∈ CN (0, σ 2 N ) is the AWGN at the base station. Using (7), the instantaneous SNR at the base station for the RF link can be obtained as…”

Section: A Snr For Vehicle-to-infrastructure Rf Linkmentioning

confidence: 99%

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On the Performance Analysis of V2N Mixed RF and Hybrid FSO/RF Communication System

et al. 2022

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Vehicular communications allow vehicles to connect with other vehicles and network infrastructures in order to facilitate the transfer of real-time information and dependable transportation. This paper proposes a system that connects vehicles to the network (base station) in two phases using transceiversequipped roadside infrastructures, such as signboards, traffic lights, and street lights, as intermediary relay nodes. In the first phase, the information transfer occurs from vehicles to infrastructure using a radio-frequency (RF) link. The second phase includes establishing communication from the infrastructure to the base station using hybrid free-space optics (FSO)/RF link. In modeling the FSO link, we consider factors like atmospheric attenuation, pointing errors, and atmospheric turbulence-induced fading, which can affect FSO performance. For this set-up, we derive the accurate expressions for the outage probability, system throughput, average symbol error rate, and average end-toend delay. Numerical results corroborate the dependency of the time allocation factor α for the vehicle-to-infrastructure link on the vehicle transmit power. Furthermore, the results elucidate the impact of the length of the transmitted packet and FSO link distance on average end-to-end delay performance. The increase in distance between infrastructure and base station can be compensated by reducing the length of packets to achieve desirable delay performance.

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Section: A Snr For Vehicle-to-infrastructure Rf Linkmentioning

confidence: 99%

“…In order to address spectrum scarcity, significant efforts have been made to enable V2X communications relying on the mmWave frequencies [7]- [10]. With mmWave access, vehicular communications can be realized with huge bandwidth and low latency, which may expedite the development of autonomous driving vehicles [11].…”

Section: Introductionmentioning

confidence: 99%

On the Performance Analysis of V2N Mixed RF and Hybrid FSO/RF Communication System

et al. 2022

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“…The results have shown that the VLC system yields significant performance. In [29], the astochastic geometry framework for caching in mmWave V2X networks is proposed and validated by Monte Carlo simulation. This framework is used to study the effect of base station density, vehicular density, and caching size on the network performance in terms of delay and connectivity.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated VLC/RF Wireless Technologies for Reliable Content Caching System in Vehicular Networks

2021

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In a vehicular communications environment, the need for information sharing, entertainment, and multimedia will increase, leading to congestion of backhaul networks. The major challenge of this network is latency and resource limitations. Proactive caching can be obtained from local caches rather than from remote servers, which can avoid long delays resulting from limited backhaul capacity and resources. Therefore, proactive caching reduces latency and improves the quality of services. Determining which files should be cached in memory is a critical issue. The paper proposes various placement schemes for caching at the vehicle and mobile base station (MBS) layers. The optimal caching place represents better user demand and latency based on the demand and mobility model. Non-cooperative and cooperative vehicles with vehicle-to-vehicle communication (V2V) are included. In both models, the high data rate of visible light communication (VLC) in the vehicular networks tends to enhance the caching capability. We also illustrate a sub-optimal algorithm to solve optimization problems compared to the optimal brute force solver and the sub-optimal genetic algorithm. Numerical results show that proactive caching schemes have a significant gain in system performance. The analysis shows that VLC improves proactive caching performance when there are less than 25 vehicles. The joint proactive caching scheme works better as the number of vehicles increases. The V2V proactive caching is useful when vehicles per platoon are ě 10 INDEX TERMS 6G, Proactive caching, vehicular networks, visible light communication (VLC), radio frequancy (RF), vehicle-to-vehicle communication (V2V), and cooperative and non-cooperative vehicles.

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“…However, one challenge in realizing IoV networks lies in the fact that the intensity of information exchanged in ITS applications ultimately results in significant backhaul overheads [4]. Apart from backhaul overheads, developing frameworks for accurate outage and finite signal-to-noise (SNR) characterization and identification of performance bounds in IoV networks remain an open research problem due to vehicular mobility, wireless channel environment, and the accurate modeling of RSU and BS placements for vehicular communications [5].…”

Section: Introductionmentioning

confidence: 99%

“…The main idea is to employ edge caching in IoV networks to reduce overall backhaul overheads by proactively storing, i.e., caching, popular contents at MEC servers located near CVs [4], e.g., cellular BSs or RSUs. In this aspect, the adoption of edge caching in IoV networks has been widely studied in the literature, e.g., [4], [5], with studies investigating the application of deep learning for edge caching and the performance of edge caching in IoV networks.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning-Based Edge Caching Strategies for Internet of Vehicles: Outage and Finite SNR Analysis

Ernest

Madhukumar

2023

IEEE Open J. Commun. Soc.

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In this paper, an ensemble learning-driven edge caching (ELDEC) strategy and a meta-based ensemble learning-driven edge caching (MELDEC) strategy are proposed for content popularity prediction and cache content placement in Internet-of-Vehicles (IoV) networks. Specifically, the proposed MELDEC and ELDEC strategies incorporate meta learning and ensemble learning for enhanced content popularity prediction in IoV networks. Closed-form outage probability and finite signal-to-noise ratio (SNR) diversity gain expressions are also derived to establish the relationship between the proposed edge caching strategies and the wireless performance of IoV networks. When compared against benchmark schemes, the proposed MELDEC and ELDEC strategies achieve near-optimal cache hit rates, outage probability, and finite SNR diversity gain under imperfect channel state information (CSI) estimation. We also show that the outage probability decay rate in the IoV network depends on the number of base stations and roadside units, and it is independent of the content popularity prediction of the MELDEC strategy, ELDEC strategy, and benchmark schemes. The performance analysis demonstrates that the proposed MELDEC and ELDEC strategies are promising solutions towards achieving reliable content access in IoV networks.

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Analytical Framework for Mmwave-Enabled V2X Caching

Cited by 9 publications

References 35 publications

On the Performance Analysis of V2N Mixed RF and Hybrid FSO/RF Communication System

On the Performance Analysis of V2N Mixed RF and Hybrid FSO/RF Communication System

Integrated VLC/RF Wireless Technologies for Reliable Content Caching System in Vehicular Networks

Ensemble Learning-Based Edge Caching Strategies for Internet of Vehicles: Outage and Finite SNR Analysis

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