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Robust Resource Allocation With Imperfect Channel Estimation in NOMA-Based Heterogeneous Vehicular Networks
Open accessAbstract
In heterogeneous vehicular networks, non-orthogonal multiple access (NOMA) with effective resource allocation improves spectrum efficiency by allowing multiple users to share the same channel. However, channel estimation errors caused by high mobility in vehicular networks affect system robustness and link reliability. As a result, resource allocation in high-mobility scenarios is a challenging issue. In this paper, robust resource allocation is studied to improve both the throughput performance and reliability of NOMA-based heterogeneous vehicular networks. A cascaded Hungarian channel assignment algorithm is proposed to simplify the formulated resource allocation problem with reliability requirements into a robust power allocation problem with chance constraints. With the approximation of non-central Chi-square distribution, the chance constraints are transformed into deterministic constraints. Furthermore, the optimal power allocation for the transformed problem is obtained in consideration of the requirements in NOMA. Simulation results illustrate the effectiv- eness of the proposed robust resource allocation scheme and its improvement over existing schemes.
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“NOMA allows multiple users to share the same time and frequency resources, and user separation is achieved in the power domain through successive interference cancellation (SIC) at the receiver.”
From Towards 6G V2X Sidelink: Survey of Resource Allocation—Mathematical Formulations, Challenges, and Proposed Solutions (2024) · cites this paper as [121]
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The Hungarian method for the assignment problem
Kuhn, H. W. (1955) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800020109 · Cited by 13,120
Research works printed but not linked: 32
- 10.2307/3315199 · DOI
- 10.1109/TWC.2013.011713.120155 · DOI
- 10.1109/TVT.2015.2480766 · DOI
- 2007, WINNER II Channel Models
- 2016, 3rd Generation Partnership Project Technical Specification Group Radio Access Network Study on LTE-based V2X Services
- 10.1109/GLOCOM.2017.8254667 · DOI
- 10.1109/ICC.2017.7996984 · DOI
- 10.1109/TWC.2018.2816942 · DOI
- 10.1109/MWC.2017.1600414 · DOI · Paper page
- 10.1109/JSAC.2017.2726018 · DOI
- 10.1109/JSAC.2011.110119 · DOI
- 10.1109/TWC.2016.2636280 · DOI
- 10.1109/TWC.2015.2509978 · DOI
- 10.1109/LWC.2017.2702747 · DOI
- 10.1109/ACCESS.2016.2646183 · DOI
- 2013, Scenarios Requirements and KPIs for 5G Mobile and Wireless System
- 10.1109/TCOMM.2015.2511078 · DOI
- 10.1109/COMST.2015.2440103 · DOI
- 10.1109/TVT.2017.2750803 · DOI
- west, 2001, Introduction to Graph Theory
- 10.1109/MVT.2017.2752798 · DOI
- 10.1109/JIOT.2016.2611605 · DOI
- 10.1109/MCOM.2013.6515060 · DOI
- 10.1109/TCOMM.2017.2699194 · DOI
- 10.1109/PIMRC.2017.8292630 · DOI
- 10.1109/JSAC.2017.2777672 · DOI
- 10.1109/TCOMM.2017.2709301 · DOI
- 10.1109/MWC.2018.1700074 · DOI
- 10.1109/JSAC.2017.2726006 · DOI
- 10.1109/MNET.2017.1600278 · DOI
- 10.1109/TCOMM.2017.2650992 · DOI
- 10.1109/GLOCOM.2015.7417061 · DOI
Cited by
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Annu, & Rajalakshmi, P. (2024) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2024.3368240 · Cited by 73
Multi-agent deep reinforcement learning based resource management in heterogeneous V2X networks
Zhao, J., Hu, F., Li, J., & Nie, Y. (2025) · Digital Communications and Networks · doi:10.1016/j.dcan.2023.06.003 · Cited by 21
Metadata
- Authors
- Guo, S., & Zhou, X.
- Affiliations
- Louisiana State University (US)
- Venue
- IEEE Transactions on Communications
- Published
- 2019-03
- Volume
- 67
- Issue
- 3
- Pages
- 2321-2332
- ISSN
- 0090-6778 (ISSN-L)
- 1558-0857
- OpenAlex
- W2904306963
- Cited by
- 77
- References
- 1
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 9 · 2023: 8 · 2022: 13 · 2021: 17 · 2020: 17 · 2019: 5
- FWCI
- 3.73
- Type
- journal article
- Language
- en
- Open access
- green
- repository.lsu.edu
- Licence
- Locations
- IEEE Transactions on Communications (publishedVersion)
- Civil War Book Review (submittedVersion)
- Funders
- National Science Foundation (1560437)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Keywords
- non-orthogonal multiple access, heterogeneous vehicular networks, robust resource allocation, channel estimation error, power allocation, chance constraints, channel assignment, noncentral chi-square distribution, high mobility
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- The paper
- Guo, S., & Zhou, X. (2019). Robust Resource Allocation With Imperfect Channel Estimation in NOMA-Based Heterogeneous Vehicular Networks. IEEE Transactions on Communications. https://doi.org/10.1109/tcomm.2018.2885999