Joint power allocation and deployment optimization for HAP-assisted NOMA–MEC system

Zhang, Yue; Na, Zhenyu; Wang, Yi; Ji, Chenglan

doi:10.1007/s11276-022-03201-8

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…This makes them better suited for certain applications when compared to UAVs. HAPs can provide computing services for vehicle users or IoT devices in rural areas or when base stations are not functioning properly [2,24,25]. They can also support UAVs in data collection [9] and wireless energy charging [26].…”

Section: Related Workmentioning

confidence: 99%

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

Nguyen

Park

2023

Mathematics

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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.

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