Resource Allocation for Fairness Enhancement in Multicell Vehicular VLC System With Optical IRS: A Cooperative Transmission Approach
In multicell downlink vehicular visible light communication (VLC) systems, vehicles at a cell edge experience lower achievable data rates compared with those at a cell center, primarily due to the weaker channel gain and intercell interference, leading to unfairness among vehicles. In this article, a cooperative transmission approach is proposed for a vehicular VLC system with optical intelligent reflecting surface (OIRS) to enhance the max-min fairness of the system by leveraging the additional OIRS-reflected channels and the interference-mitigating capabilities of cooperative transmission. To this end, the system model is established, followed by the formulation of a resource allocation problem aiming at enhancing max-min fairness, in which the minimum achievable data rate among vehicles is optimized. Then, an effective resource algorithm is proposed, transforming the original problem into an equivalent form and subsequently decomposing it into three subproblems, focusing on OIRS assignment, subchannel allocation, and power adjustment, respectively. By employing a block coordinate descent algorithm, the three subproblems are solved iteratively until convergence. In addition, simulation results confirm the improvement in max-min fairness brought by OIRS and the proposed cooperative transmission approach. Moreover, compared with various baselines, the proposed resource allocation algorithm significantly enhances in the max-min fairness for the vehicular VLC system, crucial for this application.
Authors
- Ling Cheng
- Fang Yang (ORCID: https://orcid.org/0000-0003-3575-5086)
- Zhu Han
- Chang Liu (ORCID: https://orcid.org/0000-0003-0595-4017)
- Jian Song (ORCID: https://orcid.org/0000-0002-6066-9510)
- Nan An (ORCID: https://orcid.org/0000-0002-6956-5830)
Institutions
- University of the Witwatersrand (ZA)
- China Academy of Railway Sciences (CN)
- University of Houston (US)
- Tsinghua University (CN)
Publication Details
- Journal
- IEEE Internet of Things Journal
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1109/jiot.2025.3534848
- Citations
- 3
- Primary Topic
- Optical Wireless Communication Technologies
- Type
- article
- Field-Weighted Citation Impact
- 2.19
Funders
- National Science Foundation
- Toyota Motor Corporation