Deep Reinforcement Learning-based Offloading for Latency Minimization in 3-tier V2X Networks

Dinh, Hieu; Nguyen, Nang Hung; Nguyen, Trung Thanh; Nguyen, Thanh Hung; Nguyen, Truong Thao; Nguyen, Phi Le

doi:10.1109/wcnc51071.2022.9771583

Cited by 5 publications

(1 citation statement)

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“…In [17], the authors developed a three-tier offloading architecture where vehicles can offload tasks to the RSUs and BS, and the average latency can be effectively reduced. In [18], vehicles offload tasks to the infrastructures and other vehicles to minimize the communication delay.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent-Deep-Reinforcement-Learning-Enabled Offloading Scheme for Energy Minimization in Vehicle-to-Everything Communication Systems

Duan,

Li,

Huang

et al. 2024

Electronics

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Offloading computation-intensive tasks to mobile edge computing (MEC) servers, such as road-side units (RSUs) and a base station (BS), can enhance the computation capacities of the vehicle-to-everything (V2X) communication system. In this work, we study an MEC-assisted multi-vehicle V2X communication system in which multi-antenna RSUs with liner receivers and a multi-antenna BS with a zero-forcing (ZF) receiver work as MEC servers jointly to offload the tasks of the vehicles. To control the energy consumption and ensure the delay requirement of the V2X communication system, an energy consumption minimization problem under a delay constraint is formulated. The multi-agent deep reinforcement learning (MADRL) algorithm is proposed to solve the non-convex energy optimization problem, which can train vehicles to select the beneficial server association, transmit power and offloading ratio intelligently according to the reward function related to the delay and energy consumption. The improved K-nearest neighbors (KNN) algorithm is proposed to assign vehicles to the specific RSU, which can reduce the action space dimensions and the complexity of the MADRL algorithm. Numerical simulation results show that the proposed scheme can decrease energy consumption while satisfying the delay constraint. When the RSUs adopt the indirect transmission mode and are equipped with matched-filter (MF) receivers, the proposed joint optimization scheme can decrease the energy consumption by 56.90% and 65.52% compared to the maximum transmit power and full offloading schemes, respectively. When the RSUs are equipped with ZF receivers, the proposed scheme can decrease the energy consumption by 36.8% compared to the MF receivers.

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Section: Introductionmentioning

confidence: 99%