Cooperative Communications With Relay Selection Based on Deep Reinforcement Learning in Wireless Sensor Networks

Su, Yuhan; Lu, Xiaozhen; Zhao, Yifeng; Huang, Lianfen; Du, Xiaojiang

doi:10.1109/jsen.2019.2925719

Cited by 146 publications

(73 citation statements)

References 30 publications

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“…Deep Q-Networks (DQN)algorithms are most frequently adopted in AIOT systems in recent years. A few applications of DQN and Double DQN (DDQN) in IoT communications systems are: [315], [316], [317], [318]and [319]; in IoT Cloud/Fog/Edge computing are: [320], [321], [322], [323] and [324]; in autonomous IoT robotics are: [325], [326], [327], [328] and [329] ; in IoT smart vehicles are: [330], [331] and [299] and in smart grids are: [332], [333], [334] and [335] respectively.…”

Section: E Autonomous Iotmentioning

confidence: 99%

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Naeem

Rizvi

Coronato³

2020

IEEE Access

124

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Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision.

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Section: E Autonomous Iotmentioning

confidence: 99%

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Naeem

Rizvi

Coronato³

2020

IEEE Access

124

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“…Moreover, by combining the deep neural networks with RL, deep reinforce-ment learning (DRL) [17] method has been recently attracted increasing interests in wireless communication domains. The authors in [18] proposed a DRL-based relay selection method for cooperative communication in wireless sensor networks. In [19], a DRL-based method was studied to solve the joint mode selection and resource management issue in fog radio access networks.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Deep Reinforcement Learning for Trajectory Design and Power Allocation in Multi-UAV Networks

2020

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Unmanned aerial vehicle (UAV) is regarded as an effective technology in future wireless networks. However, due to the non-convexity feature of joint trajectory design and power allocation (JTDPA) issue, it is challenging to attain the optimal joint policy in multi-UAV networks. In this paper, a multi-agent deep reinforcement learning-based approach is presented to achieve the maximum long-term network utility while satisfying the user equipments' quality of service requirements. Moreover, considering that the utility of each UAV is determined based on the network environment and other UAVs' actions, the JTDPA problem is modeled as a stochastic game. Due to the high computational complexity caused by the continuous action space and large state space, a multi-agent deep deterministic policy gradient method is proposed to obtain the optimal policy for the JTDPA issue. Numerical results indicate that our method can obtain the higher network utility and system capacity than other optimization methods in multi-UAV networks with lower computational complexity. INDEX TERMS UAV networks, trajectory design, power allocation, multi-agent deep reinforcement learning.

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“…Therefore, although the head is closer to the data source than the fixed head in centralized strategy, it has higher energy This efficiency. Meanwhile, the works in [6] point out that deep reinforcement learning (DRL) is a problem-solving tool and suitable for decentralized systems in WSNs. The existing decentralized tracking strategies in WSNs are based on the prediction position to activate a fixed number of nodes, which will result in unnecessary energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning

Xing

Zhang

et al. 2020

IEEE Sens. Lett.

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Vehicle tracking has become one of the key applications of wireless sensor networks (WSNs) in the fields of rescue, surveillance, traffic monitoring, etc. However, the increased tracking accuracy requires more energy consumption. In this letter, a decentralized vehicle tracking strategy is conceived for improving both tracking accuracy and energy saving, which is based on adjusting the intersection area between the fixed sensing area and the dynamic activation area. Then, two deep reinforcement learning (DRL) aided solutions are proposed relying on the dynamic selection of the activation area radius. Finally, simulation results show the superiority of our DRL aided design.

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Cooperative Communications With Relay Selection Based on Deep Reinforcement Learning in Wireless Sensor Networks

Cited by 146 publications

References 30 publications

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Multi-Agent Deep Reinforcement Learning for Trajectory Design and Power Allocation in Multi-UAV Networks

Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning

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