Learning-Based Resource Allocation for Backscatter-Aided Vehicular Networks
Article Properties
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DOI (url)
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Publication Date2022/10/01
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Indian UGC (journal)
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Refrences72
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Citations2
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Wali Ullah Khan Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg City, Luxembourg ORCID (unauthenticated)
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Tu N. Nguyen Department of Computer Science, Kennesaw State University, Marietta, GA, USA ORCID (unauthenticated)
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Furqan Jameel Department of Communications and Networking, Aalto University, Espoo, Finland ORCID (unauthenticated)
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Muhammad Ali Jamshed James Watt School of Engineering, University of Glasgow, Glasgow, U.K. ORCID (unauthenticated)
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Haris Pervaiz School of Computing and Communications, Lancaster University, Lancaster, U.K. ORCID (unauthenticated)
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Muhammad Awais Javed Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan ORCID (unauthenticated)
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Riku Jantti Department of Communications and Networking, Aalto University, Espoo, Finland ORCID (unauthenticated)
Cite
Khan, Wali Ullah, et al. “Learning-Based Resource Allocation for Backscatter-Aided Vehicular Networks”. IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, 2022, pp. 19676-90, https://doi.org/10.1109/tits.2021.3126766.
Khan, W. U., Nguyen, T. N., Jameel, F., Jamshed, M. A., Pervaiz, H., Javed, M. A., & Jantti, R. (2022). Learning-Based Resource Allocation for Backscatter-Aided Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems, 23(10), 19676-19690. https://doi.org/10.1109/tits.2021.3126766
Khan, Wali Ullah, Tu N. Nguyen, Furqan Jameel, Muhammad Ali Jamshed, Haris Pervaiz, Muhammad Awais Javed, and Riku Jantti. “Learning-Based Resource Allocation for Backscatter-Aided Vehicular Networks”. IEEE Transactions on Intelligent Transportation Systems 23, no. 10 (2022): 19676-90. https://doi.org/10.1109/tits.2021.3126766.
Khan WU, Nguyen TN, Jameel F, Jamshed MA, Pervaiz H, Javed MA, et al. Learning-Based Resource Allocation for Backscatter-Aided Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems. 2022;23(10):19676-90.
Journal Categories
Technology
Electrical engineering
Electronics
Nuclear engineering
Electric apparatus and materials
Electric circuits
Electric networks
Technology
Engineering (General)
Civil engineering (General)
Technology
Engineering (General)
Civil engineering (General)
Transportation engineering
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Energy-efficient backscatter aided uplink NOMA roadside sensor communications under channel estimation errors | 2021 | |||
| Backscatter sensors communication for 6G low-powered NOMA-enabled IoT networks under imperfect SIC | 2021 | |||
| Integration of backscatter communication with multi-cell NOMA: A spectral efficiency optimization under imperfect SIC | 2021 | |||
| Deep reinforcement learning for time scheduling in RF-powered backscatter cognitive radio networks | 2018 | |||
| Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms | Wireless Networks |
| 95 | 2021 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| A Hybrid Deep Learning Approach for Bottleneck Detection in IoT | IEEE Access |
| 2022 | |
Automated Facial Expression Recognition Framework Using Deep Learning | Journal of Healthcare Engineering |
| 7 | 2022 |
Citations Analysis
The category
Technology: Electrical engineering. Electronics. Nuclear engineering 1
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled A Hybrid Deep Learning Approach for Bottleneck Detection in IoT and was published in 2022. The most recent citation comes from a 2022 study titled A Hybrid Deep Learning Approach for Bottleneck Detection in IoT. This article reached its peak citation in 2022, with 2 citations. It has been cited in 2 different journals, 50% of which are open access. Among related journals, the IEEE Access cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.