Reinforcement Learning Based Matching for Computation Offloading in D2D Communications

Tian, Bingxin; Wang, Li; Ai, Yutong; Fei, Aiguo

doi:10.1109/iccchina.2019.8855817

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…For a given workload, to utilize only one or sever servers at the full state and turn off the other servers may result in low energy consumption, but on the other hand contribute to the high delay. Therefore, how to allocate the computation resources to balance energy consumption and service quality is an important direction in the research [149], [241], [242].…”

Section: A Power Consumption Modelingmentioning

confidence: 99%

AI based Service Management for 6G Green Communications

Mao,

Tang,

Yuichi

et al. 2021

Preprint

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Green communications have always been a target for the information industry to alleviate energy overhead and reduce fossil fuel usage. In current 5G and future 6G era, there is no doubt that the volume of network infrastructure and the number of connected terminals will keep exponentially increasing, which results in the surging energy cost. It becomes growing important and urgent to drive the development of green communications. However, 6G will inevitably have increasingly stringent and diversified requirements for Quality of Service (QoS), security, flexibility, and even intelligence, all of which challenge the improvement of energy efficiency. Moreover, the dynamic energy harvesting process, which will be adopted widely in 6G, further complicates the power control and network management. To address these challenges and reduce human intervene, Artificial Intelligence (AI) has been widely recognized and acknowledged as the only solution. Academia and industry have conducted extensive research to alleviate energy demand, improve energy efficiency, and manage energy harvesting in various communication scenarios. In this paper, we present the main considerations for green communications and survey the related research on AI-based green communications. We focus on how AI techniques are adopted to manage the network and improve energy harvesting toward the green era. We analyze how state-of-the-art Machine Learning (ML) and Deep Learning (DL) techniques can cooperate with conventional AI methods and mathematical models to reduce the algorithm complexity and optimize the accuracy rate to accelerate the applications in 6G. Finally, we discuss the existing problems and envision the challenges for these emerging techniques in 6G.

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Section: A Power Consumption Modelingmentioning

confidence: 99%

AI based Service Management for 6G Green Communications

Mao,

Tang,

Yuichi

et al. 2021

Preprint

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“…It is difficult to obtain large amounts of data to train ML models. Other studies believe that Reinforcement Learning (RL) is a potential solution to task offloading [18][19][20]. RL-based algorithms enable vehicles to learn an optimal offloading strategy by interacting with the environment.…”

Section: Introductionmentioning

confidence: 99%

MESON: A Mobility-Aware Dependent Task Offloading Scheme for Urban Vehicular Edge Computing

Zhao

Zhang

Wan³

et al. 2024

IEEE Trans. on Mobile Comput.

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Vehicular Edge Computing (VEC) is the transportation version of Mobile Edge Computing (MEC) in road scenarios. One key technology of VEC is task offloading, which allows vehicles to send their computation tasks to the surrounding Roadside Units (RSUs) or other vehicles for execution, thereby reducing computation delay and energy consumption. However, the existing task offloading schemes still have various gaps and face challenges that should be addressed because vehicles with time-varying trajectories need to process massive data with high complexity and diversity. In this paper, a VEC-based computation offloading model is developed with consideration of data dependency of tasks. The minimization of the average response time and average energy consumption of the system is defined as a combinatorial optimization problem. To solve this problem, we propose a Mobility-aware dependent task offloading (MESON) Scheme for urban VEC and develop a DRL-based algorithm to train the offloading strategy. To improve the training efficiency, a vehicle mobility detection algorithm is further designed to detect the communication time between vehicles and RSUs. In this way, MESON can avoid unreasonable decisions by lowering the size of the action space. Moreover, to improve the system stability and the offloading successful rate, we design a task priority determination scheme to prioritize the tasks in the waiting queue. The experimental results show that MESON is superior compared to other task offloading schemes in terms of the average response time, average system energy consumption, and offloading successful rate.

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