Antenna Beamwidth Optimization in Directional Device-to-Device Communication Using Multi-Agent Deep Reinforcement Learning

IEEE Access ◽

10.1109/access.2021.3102230 ◽

2021 ◽

pp. 1-1

Author(s):

Niloofar Bahadori ◽

Mahmoud Nabil ◽

Abdollah Homaifar

Keyword(s):

Reinforcement Learning ◽

Device To Device ◽

Multi Agent ◽

Device To Device Communication

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Resource Allocation and Power Control Policy for Device-to-Device Communication Using Multi-Agent Reinforcement Learning

Computers Materials & Continua ◽

10.32604/cmc.2020.09130 ◽

2020 ◽

Vol 63 (3) ◽

pp. 1515-1532

Author(s):

Yifei Wei ◽

Yinxiang Qu ◽

Min Zhao ◽

Lianping Zhang ◽

F. Richard Yu

Keyword(s):

Resource Allocation ◽

Reinforcement Learning ◽

Power Control ◽

Control Policy ◽

Device To Device ◽

Multi Agent ◽

Device To Device Communication

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An Actor-Critic Reinforcement Learning for Device-to-Device Communication Underlaying Cellular Network

TENCON 2018 - 2018 IEEE Region 10 Conference ◽

10.1109/tencon.2018.8650160 ◽

2018 ◽

Author(s):

Pratap Khuntia ◽

Ranjay Hazra

Keyword(s):

Reinforcement Learning ◽

Cellular Network ◽

Device To Device ◽

Device To Device Communication

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Throughput-Aware Cooperative Reinforcement Learning for Adaptive Resource Allocation in Device-to-Device Communication

Future Internet ◽

10.3390/fi9040072 ◽

2017 ◽

Vol 9 (4) ◽

pp. 72 ◽

Author(s):

Muhidul Khan ◽

Muhammad Alam ◽

Yannick Moullec ◽

Elias Yaacoub

Keyword(s):

Resource Allocation ◽

Reinforcement Learning ◽

Adaptive Resource Allocation ◽

Device To Device ◽

Device To Device Communication ◽

Adaptive Resource

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An Efficient Reinforcement Learning for Device-to-device Communication Underlaying Cellular Network

IEIE Transactions on Smart Processing and Computing ◽

10.5573/ieiespc.2020.9.1.075 ◽

2020 ◽

Vol 9 (1) ◽

pp. 75-84

Author(s):

Pratap Khuntia ◽

Ranjay Hazra

Keyword(s):

Reinforcement Learning ◽

Cellular Network ◽

Device To Device ◽

Device To Device Communication

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An efficient actor-critic reinforcement learning for device-to-device communication underlaying sectored cellular network

International Journal of Communication Systems ◽

10.1002/dac.4315 ◽

2020 ◽

Vol 33 (10) ◽

pp. e4315

Author(s):

Pratap Khuntia ◽

Ranjay Hazra ◽

Peter Chong

Keyword(s):

Reinforcement Learning ◽

Cellular Network ◽

Device To Device ◽

Device To Device Communication

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An efficient Deep reinforcement learning with extended Kalman filter for device‐to‐device communication underlaying cellular network

Transactions on Emerging Telecommunications Technologies ◽

10.1002/ett.3671 ◽

2019 ◽

Vol 30 (9) ◽

Author(s):

Pratap Khuntia ◽

Ranjay Hazra

Keyword(s):

Kalman Filter ◽

Reinforcement Learning ◽

Extended Kalman Filter ◽

Cellular Network ◽

Device To Device ◽

Device To Device Communication

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Cooperative reinforcement learning for adaptive power allocation in device-to-device communication

2018 IEEE 4th World Forum on Internet of Things (WF-IoT) ◽

10.1109/wf-iot.2018.8355169 ◽

2018 ◽

Author(s):

Muhidul Islam Khan ◽

Muhammad Mahtab Alam ◽

Yannick Le Moullec ◽

Elias Yaacoub

Keyword(s):

Reinforcement Learning ◽

Power Allocation ◽

Device To Device ◽

Device To Device Communication ◽

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Multi-Agent Deep Reinforcement Learning for Decentralized Cooperative Traffic Signal Control

10.1061/9780784483053.039 ◽

2020 ◽

Author(s):

Yang Zhao ◽

Jian-Ming Hu ◽

Ming-Yang Gao ◽

Zuo Zhang

Keyword(s):

Reinforcement Learning ◽

Traffic Signal ◽

Signal Control ◽

Traffic Signal Control ◽

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Optical Antenna A Key Enabling Arial For Device To Device Communication

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i8.306309 ◽

2018 ◽

Vol 6 (8) ◽

pp. 306-309

Author(s):

Anand Mohan

Keyword(s):

Optical Antenna ◽

Device To Device ◽

Device To Device Communication

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Output feedback reinforcement learning based optimal output synchronisation of heterogeneous discrete-time multi-agent systems

IET Control Theory and Applications ◽

10.1049/iet-cta.2018.6266 ◽

2019 ◽

Vol 13 (17) ◽

pp. 2866-2876

Author(s):

Syed Ali Asad Rizvi ◽

Zongli Lin

Keyword(s):

Reinforcement Learning ◽

Discrete Time ◽

Output Feedback ◽

Multi Agent Systems ◽

Agent Systems ◽

Optimal Output ◽

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