Scheduling Phase Period Design for Sub-6 GHz Assisted Millimeter Wave Vehicle-to-Everything (V2X) Communication Scheduler
The extensive sharing of sensing data through Vehicle-to-Everything (V2X) communication technology is crucial for enhancing the performance of intelligent transportation systems (ITS). Millimeter wave (mmWave) V2X is a promising direction for achieving high-rate, low-latency data transmission. In our previous work, we proposed a sub-6 GHz assisted mmWave V2X communication architecture to address the challenges of mmWave peer discovery and beam alignment. Additionally, we formulated a novel optimization problem for mmWave link scheduling that thoroughly considered realistic vehicular data features, i.e., data importance and freshness. However, a gap still remains in our previous solutions, such as how to design the mmWave link scheduling phase. In this paper, we study the impact of the scheduling phase period on the optimality of the mmWave scheduling problem. This problem is non-trivial as there exists a trade-off between the freshness of information and the utilization of channel resources. We derive a series of sufficient conditions on the scenario where the increase of scheduling phase period leads to a decrease in network utility. Extensive simulation studies are conducted to verify the theoretical results.
Authors
- Chenyuan He (ORCID: https://orcid.org/0000-0002-9378-7935)
- Yingfeng Cai (ORCID: https://orcid.org/0000-0002-0633-9887)
- Fenghua Huang (ORCID: https://orcid.org/0000-0003-2360-9532)
- Zhouyu Zhang (ORCID: https://orcid.org/0000-0001-8047-7746)
- Long Chen (ORCID: https://orcid.org/0000-0003-0184-5446)
- Hai Wang (ORCID: https://orcid.org/0000-0001-7752-1013)
Institutions
- Jiangsu University (CN)
- Yango University (CN)
Publication Details
- Journal
- IEEE Transactions on Vehicular Technology
- Published
- 2024-12-24
- DOI
- https://doi.org/10.1109/tvt.2024.3521941
- Citations
- 3
- Primary Topic
- Vehicular Ad Hoc Networks (VANETs)
- Type
- article
- Field-Weighted Citation Impact
- 0.63