HAP-Assisted RSMA-Enabled Vehicular Edge Computing: A DRL-Based Optimization Framework

Tri-Hai Nguyen and Laihyuk Park ()
Additional contact information
Tri-Hai Nguyen: Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Laihyuk Park: Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea

Mathematics, 2023, vol. 11, issue 10, 1-23

Abstract: In recent years, the demand for vehicular edge computing (VEC) has grown rapidly due to the increasing need for low-latency and high-throughput applications such as autonomous driving and smart transportation systems. Nevertheless, offering VEC services in rural locations remains a difficulty owing to a lack of network facilities. We tackle this issue by taking advantage of high-altitude platforms (HAPs) and rate-splitting multiple access (RSMA) techniques to propose an HAP-assisted RSMA-enabled VEC system, which can enhance connectivity and provide computational capacity in rural locations. We also introduce a deep deterministic policy gradient (DDPG)-based framework that optimizes the allocation of resources and task offloading by jointly considering the offloading rate, splitting rate, transmission power, and decoding order parameters. Via results from extensive simulations, the proposed framework shows superior performance in comparison with conventional schemes regarding task success rate and energy consumption.

Keywords: computation offloading; deep reinforcement learning; high-altitude platform; rate-splitting multiple access; vehicular edge computing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/10/2376/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/10/2376/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:10:p:2376-:d:1151421

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().