Adaptive task offloading in V2X networks based on deep reinforcement learning

Dengsong Yang; Baili Ni; Hao Qin; Fei Ma

doi:10.1117/12.2627783

14 March 2022 Adaptive task offloading in V2X networks based on deep reinforcement learning

Dengsong Yang, Baili Ni, Hao Qin, Fei Ma

Proceedings Volume 12165, International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2021); 1216526 (2022) https://doi.org/10.1117/12.2627783
Event: 2021 International Conference on Intelligent Traffic Systems and Smart City, 2021, Zhengzhou, China

Abstract

With the constraints of limited computing and communication resources in V2X networks, V2V communication for task offloading needs to adapt to the dynamic V2X environment while satisfying the requirements of low delay and high reliability. Aiming at balancing the trade-off between delay and power consumption in V2V-enabled task offloading, this paper proposes a V2V-enabled multi vehicle task offloading algorithm based on deep reinforcement learning Deep Deterministic Policy Gradient (DDPG). Firstly, we construct a V2V-enabled multi vehicle task offloading system model, which leverages the surrounding vehicles with computing resources as the relay node. In this model, multi hop communication is used to offload tasks to multiple vehicles for coordinated computation, and we assume the transmission power of tasks is a tunable continuous variable. Then, DDPG algorithm is proposed to deal with the continuous high-dimensional action space in task offloading. The proposed task offloading algorithm based on DDPG is simulated and compared with other deep reinforcement learning algorithms. Experimental results show that the algorithm can effectively balance the delay and power performance while improving the success rate of task offloading.

Citation Download Citation

Dengsong Yang, Baili Ni, Hao Qin, and Fei Ma "Adaptive task offloading in V2X networks based on deep reinforcement learning", Proc. SPIE 12165, International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2021), 1216526 (14 March 2022); https://doi.org/10.1117/12.2627783

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KEYWORDS

Relays

Signal to noise ratio

Systems modeling

Telecommunications

Computer simulations

Reliability

Detection and tracking algorithms

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