Relay selection algorithm based on social network combined with Q‐learning for vehicle D2D communication

Qian, Hongzhi; Yu, Jiadi; Hua, Licheng

doi:10.1049/iet-com.2019.0419

Cited by 10 publications

(2 citation statements)

References 28 publications

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“…Recently, many studies have investigated application of reinforcement learning (RL) in the field of resource management for wireless social networks [30]- [35]. Qian et al in [31] proposed an optimal relay selection approach based on Q-learning to maximize the sum rate of D2D links in social networks. In [32], a novel big data deep reinforcement learning (DRL) approach was studied to optimize resource management in mobile trust-based social networks, where the intrinsic nature of social networks was exploited to generate effective resource management rules.…”

Section: Introductionmentioning

confidence: 99%

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Deep-Reinforcement-Learning-Based Energy-Efficient Resource Management for Social and Cognitive Internet of Things

Yang

Zhong

Chen

et al. 2020

IEEE Internet Things J.

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Internet of things (IoT) has attracted much interest due to its wide applications such as smart city, manufacturing, transportation, and healthcare. Social and cognitive IoT is capable of exploiting the social networking characteristics to optimize the network performance. Considering the fact that IoT devices have different quality of service (QoS) requirements (ranging from ultra-reliable and low-latency communications (URLLC) to minimum data rate), this paper presents a QoS-driven socialaware enhanced device-to-device (D2D) communication network model for social and cognitive IoT by utilizing social orientation information. We model the optimization problem as a multi-agent reinforcement learning formulation, and a novel coordinated multi-agent deep reinforcement learning based resource management approach is proposed to optimize the joint radio block assignment and transmission power control strategy. Meanwhile, prioritized experience replay (PER) and coordinated learning mechanisms are employed to enable communication links to work cooperatively in a distributed manner, which enhances the network performance and access success probability. Simulation results corroborate the superiority in the performance of the presented resource management approach, and it outperforms other existing approaches in terms of meeting the energy efficiency and the QoS requirements.

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Section: Introductionmentioning

confidence: 99%

“…The references [21], [34], and [35] proposed RL based mobile resource management mechanism to guarantee diverse vehicular social requirements under dynamic environments. However, most of the works [31]- [35] did not consider the stringent reliability and latency constraints into the optimization problem.…”

Section: Introductionmentioning

confidence: 99%