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Joint CB and SIC Technology to Optimize Throughput and Cost Under IoV.
- Source :
-
IEEE Transactions on Vehicular Technology . Aug2022, Vol. 71 Issue 8, p8689-8701. 13p. - Publication Year :
- 2022
-
Abstract
- Most researches on the Internet of Vehicles (IoV) in the 5G era are focused on the low latency and the massive transmitted data. However, the 5G base station has relatively a small coverage and a high cost, which is the main reason to hinder the development of the 5G applications on the IoV. In this paper, we try to put forward some feasible solutions for this problem. We will use two physical layer communication techniques, the Coherent Beamforming (CB) technique and the Successive Interference Cancellation (SIC) technique, to jointly optimize the throughput of vehicles data transmission and the network infrastructure cost. We first establish the mathematical model based on CB and SIC technology and prove that it cannot be solved directly. Then, we design the Road and CB-nodes Assignment (RCA) algorithm and the Uninstalling Data Scheduling (UDS) algorithm to solve the problem. RCA is based on CB and SIC technology to reasonably arrange CB-nodes, so that CB-nodes set can send data from vehicles to the base station and the base station can receive multiple sets of data at the same time. UDS schedules vehicles based on various factors to optimize the total amount of data transferred by vehicles and improve throughput. In simulations, we conduct some comparative experiments, which are the not using Algorithm 3 scheme, the CB scheme, the SIC scheme, the common scheme and the Relay Task Offloading Scheme in Vehicular Edge Computing algorithm (RVEC Algorithm). Results show that our proposed method has the advantages of cost saving and throughput improvement. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189545
- Volume :
- 71
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Vehicular Technology
- Publication Type :
- Academic Journal
- Accession number :
- 158604205
- Full Text :
- https://doi.org/10.1109/TVT.2022.3175835