Learning-Based Resource Allocation for Backscatter-Aided Vehicular Networks

Khan, Wali Ullah; Nguyen, Tu N.; Jameel, Furqan; Jamshed, Muhammad Ali; Pervaiz, Haris; Javed, Muhammad Awais; Jäntti, Riku

doi:10.1109/tits.2021.3126766

Cited by 40 publications

(8 citation statements)

References 69 publications

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“…Also, B = 2hw + 2hl + lw is the total antenna elements in the proposed 3-D array. The CIR of 3-D array can be achieved by inserting Equation (3) into Equation (1). When the elevation angle in 3-D space is ignored (i.e.…”

Section: Proposed 3-d Gbsm V2v System Modelmentioning

confidence: 99%

“…Recently wireless mobile communications in underground confined space scenarios of mines, tunnels, and shopping plazas have become prominent. Wireless communication technologies such as multiple-input multiple-output (MIMO), cooperative communication, channel coding, internet-of-things (IoT), webof-things (WoT), and content-aware big social data have recently played an important role in the development of future fifth-generation (5G) communication networks [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Retracted: Non‐stationary 3‐D GBSM channel model for V2V communications

Saleem

Asif

et al. 2022

IET Communications

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The above article from IET Communications, published online on 17 October 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

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Section: Proposed 3-d Gbsm V2v System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Retracted: Non‐stationary 3‐D GBSM channel model for V2V communications

Saleem

Asif

et al. 2022

IET Communications

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“…Furthermore the most important elements within the model affords huge benefits over conventional rule-based methods in classifying the problem of phishing simulations is known as Collaborative Learning Algorithm. Training results actually show that anti-phishing training must be captured on the training of character users in preference on huge consumer attacks [10]. Additionally presenting a promising phishing detection system training model for predicting phishing susceptibility.…”

Section: Phishing Attack Detection Using Collaborative Learningmentioning

confidence: 98%

Detection and Prevention of Phishing Attacks in DDoS Using Collaborative Learning Algorithm

Cheena¹,

Rammohan²

2023

IJRASET

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Threat to cyber security are significant in providing phishing attacks with huge industry area with anti phishing simulations. Thus it minimizes risk taken infuses attacks with phishing. Large scale phishing training participates various simulations based on phishing attacks. In our proposed system phishing attacks will be analysed based on its credentials and simulated with training data results. They are developed along with data driven models for classification of users perceiving such phishing attacks. Besides analyzation of results based on huge attacks on phishing and training them against those phishing data users most clicking behaviour will be monitored regularly. Cyber phishing attacks in DDoS are significant in IoT are threat in internet environment and predicts attacks through collaborative learning algorithm. Attacked systems connected through onine processes malicious actions without any authorized or authenticated attacks. Further phishing techniques will be predicted using training data and updated on machine learning using Collaborative Learning Algorithm.

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“…However, the researchers cannot identify optimization opportunities for this class of networks. This study [137] proposed a learning method to deal with this optimization issue. The authors presented a strategy for resource allocation and user association for vehicle networks that consider collaboration-centric spectrum sharing.…”

Section: Ai-based Bc Involving Ris Uav and Vehicular Networkmentioning

confidence: 99%

The State of AI-Empowered Backscatter Communications: A Comprehensive Survey

Ahmed¹,

Hussain²,

Ali³

et al. 2023

Preprint

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<p>This paper brings these two technologies together to investigate the current state of AI-powered BC. </p> <p>We begin with an introduction to BC and an overview of the AI algorithms employed in BC. Then, we delve into the recent advances in AI-based BC, covering key areas such as backscatter signal detection, channel estimation, and jammer control to ensure security, mitigate interference, and improve throughput and latency. We also explore the exciting frontiers of AI in BC using B5G/6G technologies, including backscatter-assisted relay and cognitive communication networks, backscatter-assisted MEC networks, and BC with RIS, UAV, and vehicular networks. Finally, we highlight the challenges and present new research opportunities in AI-powered BC. This survey provides a comprehensive overview of the potential of AI-powered BC and its insightful impact on the future of IoT.</p>

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Learning-Based Resource Allocation for Backscatter-Aided Vehicular Networks

Cited by 40 publications

References 69 publications

Retracted: Non‐stationary 3‐D GBSM channel model for V2V communications

Retracted: Non‐stationary 3‐D GBSM channel model for V2V communications

Detection and Prevention of Phishing Attacks in DDoS Using Collaborative Learning Algorithm

The State of AI-Empowered Backscatter Communications: A Comprehensive Survey

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