DDPG-Based Joint Resource Management for Latency Minimization in NOMA-MEC Networks

Wang, Jiayin; Wang, Yafeng; Cheng, Peng; Yu, Kuanxin; Wang, Xiang

doi:10.1109/lcomm.2023.3266931

Cited by 17 publications

(2 citation statements)

References 12 publications

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“…This algorithm combines the representational capabilities of deep learning with the decision optimization techniques of policy gradient methods, adopting a variant of the Actor-Critic architecture, implemented through deep neural networks to approximate both policy and value functions [29]. The actor network directly maps states to deterministic actions, while the Critic network evaluates the expected return for given states and actions [30]. Additionally, DDPG incorporates an experience replay mechanism, storing past transitions (states, actions, rewards, and new states) for reuse during training, thus reducing correlations between samples and enhancing learning stability.…”

Section: Buy Tmentioning

confidence: 99%

Multi-Time-Scale Optimal Scheduling Strategy for Marine Renewable Energy Based on Deep Reinforcement Learning Algorithm

Xu,

Lin,

Shao

et al. 2024

Entropy

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Surrounded by the Shandong Peninsula, the Bohai Sea and Yellow Sea possess vast marine energy resources. An analysis of actual meteorological data from these regions indicates significant seasonality and intra-day uncertainty in wind and photovoltaic power generation. The challenge of scheduling to leverage the complementary characteristics of various renewable energy sources for maintaining grid stability is substantial. In response, we have integrated wave energy with offshore photovoltaic and wind power generation and propose a day-ahead and intra-day multi-time-scale rolling optimization scheduling strategy for the complementary dispatch of these three energy sources. Using real meteorological data from this maritime area, we employed a CNN-LSTM neural network to predict the power generation and load demand of the area on both day-ahead 24 h and intra-day 1 h time scales, with the DDPG algorithm applied for refined electricity management through rolling optimization scheduling of the forecast data. Simulation results demonstrate that the proposed strategy effectively meets load demands through complementary scheduling of wave power, wind power, and photovoltaic power generation based on the climatic characteristics of the Bohai and Yellow Sea regions, reducing the negative impacts of the seasonality and intra-day uncertainty of these three energy sources on the grid. Additionally, compared to the day-ahead scheduling strategy alone, the day-ahead and intra-day rolling optimization scheduling strategy achieved a reduction in system costs by 16.1% and 22% for a typical winter day and a typical summer day, respectively.

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Section: Buy Tmentioning

confidence: 99%

Multi-Time-Scale Optimal Scheduling Strategy for Marine Renewable Energy Based on Deep Reinforcement Learning Algorithm

Xu,

Lin,

Shao

et al. 2024

Entropy

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“…The Deep Deterministic Policy Gradient (DDPG) algorithm [ 29 ], where the resource allocation problem is solved via the DDPG method.…”

Section: Simulation Analysismentioning

confidence: 99%

Intelligent Resource Allocation for V2V Communication with Spectrum–Energy Efficiency Maximization

Xu,

Wang,

Song

et al. 2023

Sensors

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Aiming to address the limitations of traditional resource allocation algorithms in the Internet of Vehicles (IoV), whereby they cannot meet the stringent demands for ultra-low latency and high reliability in vehicle-to-vehicle (V2V) communication, this paper proposes a wireless resource allocation algorithm for V2V communication based on the multi-agent deep Q-network (MDQN). The system model utilizes 5G network slicing technology as its fundamental feature and maximizes the weighted spectrum–energy efficiency (SEE) while satisfying reliability and latency constraints. In this approach, each V2V link is treated as an agent, and the state space, action, and reward function of MDQN are specifically designed. Through centralized training, the neural network parameters of MDQN are determined, and the optimal resource allocation strategy is achieved through distributed execution. Simulation results demonstrate the effectiveness of the proposed scheme in significantly improving the SEE of the network while maintaining a certain success rate for V2V link load transmission.

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Dependency-aware cache optimization and offloading strategies for intelligent transportation systems

Zhu,

Song,

Huang

et al. 2024

J Supercomput

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DDPG-Based Joint Resource Management for Latency Minimization in NOMA-MEC Networks

Cited by 17 publications

References 12 publications

Multi-Time-Scale Optimal Scheduling Strategy for Marine Renewable Energy Based on Deep Reinforcement Learning Algorithm

Multi-Time-Scale Optimal Scheduling Strategy for Marine Renewable Energy Based on Deep Reinforcement Learning Algorithm

Intelligent Resource Allocation for V2V Communication with Spectrum–Energy Efficiency Maximization

Dependency-aware cache optimization and offloading strategies for intelligent transportation systems

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