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Resource Allocation in Information-centric Wireless Networking with D2D-enabled MEC: A Deep Reinforcement Learning Approach

Dan Wang, Hao Qin, Bin Song, Xiaojiang Du, Mohsen Guizani
2019 IEEE Access  
In addition, we use softmax to output channel selection to maximize system capacity and spectrum efficiency while avoiding interference to cellular users.  ...  This paper investigates the optimal policy for resource allocation in ICWNs by maximizing the spectrum efficiency and system capacity of the overall network.  ...  In Algorithm 1, we run Algorithm 2 to learn channel selection and power control policies.  ... 
doi:10.1109/access.2019.2935545 fatcat:ceo46gco7zegfnkgflxho4ruwy

Non-Cooperative Energy Efficient Power Allocation Game in D2D Communication: A Multi-Agent Deep Reinforcement Learning Approach

Khoi Khac Nguyen, Trung Q Duong, Ngo Anh Vien, Nhien-An Le-Khac, Nghia M Nguyen
2019 IEEE Access  
INDEX TERMS Energy efficient wireless communication, power allocation, D2D communication, multiagent reinforcement learning, deep reinforcement learning.  ...  Our main contribution is to propose a novel non-cooperative and real-time approach based on deep reinforcement learning to deal with the energy-efficient power allocation problem while still satisfying  ...  REINFORCEMENT LEARNING FOR ENERGY EFFICIENT POWER ALLOCATION GAME IN D2D COMMUNICATION In this section, we discuss the background of singleagent reinforcement learning and multi-agent reinforcement learning  ... 
doi:10.1109/access.2019.2930115 fatcat:vuyjpjpumzamxhlulczi4hipwy

Deep Reinforcement Learning for Resource Allocation in V2V Communications [article]

Hao Ye, Geoffrey Ye Li
2017 arXiv   pre-print
In this article, we develop a decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communication systems based on deep reinforcement learning.  ...  Each V2V link is considered as an agent, making its own decisions to find optimal sub-band and power level for transmission.  ...  Kathiravetpillai Sivanesan, and Dr. JoonBeom Kim from Intel Corporation for their insightful comments, which have substantially improved the quality of this paper.  ... 
arXiv:1711.00968v2 fatcat:thdp3lil5rcbnhhkp3m2er6kq4

QSPCA: A Two-Stage Efficient Power Control Approach in D2D Communication for 5G Networks

Saurabh Chandra, Prateek, Rohit Sharma, Rajeev Arya, Korhan Cengiz
2021 Intelligent and Converged Networks  
Accordingly, a two-stage transmit power control approach (named QSPCA) is proposed: First, a reinforcement Q-learning based power control technique and; second, a supervised learning based support vector  ...  There is a need for rigorous analyses on the policy improvement and evaluation of network performance.  ...  Acknowledgment This work was supported by the Science and Engineering Research Board (SERB-DST), Govt. of India (No. EEQ/2019/000010).  ... 
doi:10.23919/icn.2021.0021 fatcat:vac5dwna6zavtiisfdts5lfbza

Dynamic video delivery using deep reinforcement learning for device-to-device underlaid cache-enabled Internet-of-vehicle networks

Minseok Choi, Myungjae Shin, Joongheon Kim
2021 Journal of Communications and Networks  
providing contents, 2) power allocation for D2D users, and 3) power allocation for cellular vehicles.  ...  For each cache-enabled vehicle, the expected cost is obtained from the stochastic shortest path problem that is solved by deep reinforcement learning without the knowledge of global channel state information  ...  Deep Q-network (DQN) The deep Q-network (DQN) is one of the breakthrough deep reinforcement algorithms for applying a neural network to reinforcement learning.  ... 
doi:10.23919/jcn.2021.000006 fatcat:ixjlbk2cj5b7pojytspvuxuzd4

Distributed Deep Deterministic Policy Gradient for Power Allocation Control in D2D-Based V2V Communications

Khoi Khac Nguyen, Trung Q. Duong, Ngo Anh Vien, Nhien-An Le-Khac, Long D. Nguyen
2019 IEEE Access  
Currently, deep reinforcement learning is rising as a powerful tool to enable each node in the network to have a real-time self-organising ability.  ...  INDEX TERMS Non-cooperative D2D communication, D2D-based V2V communications, power allocation, multi-agent deep reinforcement learning, and deep deterministic policy gradient (DDPG).  ...  Meanwhile, the authors in [8] proposed a grouping algorithm, channel selection, and power control strategies to maximise the performance of a network consisting of multiple D2D-based V2V links sharing  ... 
doi:10.1109/access.2019.2952411 fatcat:hguldp3debfzheugwiane3ptai

Multi-Agent Deep Reinforcement Learning based Spectrum Allocation for D2D Underlay Communications [article]

Zheng Li, Caili Guo
2019 arXiv   pre-print
In this paper, a distributed spectrum allocation framework based on multi-agent deep reinforcement learning is proposed, named multi-agent actor critic (MAAC).  ...  In this situation, D2D transmission may cause severe interference to both the cellular and other D2D links, which imposes a great technical challenge to spectrum allocation.  ...  In the future work, we plan to combine the proposed approaches with continuous-valued power control, and design an integrated deep reinforcement learning framework that automatically selects RB and transmit  ... 
arXiv:1912.09302v1 fatcat:64kevc7xxnf45kcve2cc22ua6q

Reinforcement Learning for Energy Optimization with 5G Communications in Vehicular Social Networks

Hyebin Park, Yujin Lim
2020 Sensors  
To make an optimal mode selection and power control, it is necessary to apply reinforcement learning that considers a variety of factors.  ...  However, D2D communication is highly affected by interference and therefore requires interference-management techniques, such as mode selection and power control.  ...  .: conceptualization, methodology, writing-review and editing, supervision, project administration, funding acquisition; All authors have read and agreed to the published version of the manuscript.  ... 
doi:10.3390/s20082361 pmid:32326250 fatcat:e63p7aswl5debidzqztpcwkeku

Deep Reinforcement Learning Based Mode Selection and Resource Management for Green Fog Radio Access Networks [article]

Yaohua Sun, Mugen Peng, Shiwen Mao
2018 arXiv   pre-print
Motivated by the recent development of artificial intelligence, a deep reinforcement learning (DRL) based joint mode selection and resource management approach is proposed.  ...  The core idea is that the network controller makes intelligent decisions on UE communication modes and processors' on-off states with precoding for UEs in C-RAN mode optimized subsequently, aiming at minimizing  ...  is known as deep reinforcement learning (DRL).  ... 
arXiv:1809.05629v1 fatcat:2kptt77ucffz5cvzrxgmpiees4

A Multi-Agent Deep Reinforcement Learning based Spectrum Allocation Framework for D2D Communications [article]

Zheng Li, Caili Guo, Yidi Xuan
2019 arXiv   pre-print
In this paper, a distributed spectrum allocation framework based on multi-agent deep reinforcement learning is proposed, named Neighbor-Agent Actor Critic (NAAC).  ...  The simulation results show that the proposed framework can effectively reduce the outage probability of cellular links, improve the sum rate of D2D links and have good convergence.  ...  Reinforcement learning (RL) is one of the most powerful machine learning tools for policy control [8] .  ... 
arXiv:1904.06615v3 fatcat:omc3adi4wrhe3lh46alxeca4m4

Deep-Learning-Based Resource Allocation for Time-Sensitive Device-to-Device Networks

Zhe Zheng, Yingying Chi, Guangyao Ding, Guanding Yu
2022 Sensors  
Then, we propose a deep learning (DL)-based resource management framework using deep neural network (DNN).  ...  In this paper, we investigate the resource allocation problem in the time-sensitive D2D network where the latency and reliability performance is modeled by the achievable rate in the short blocklength  ...  In [24] , the channel selection and transmit power of the D2D pairs are jointly optimized to achieve a higher weighted sum rate by using deep reinforcement learning.  ... 
doi:10.3390/s22041551 pmid:35214450 pmcid:PMC8876950 fatcat:uhd3mrv4hndgjej7ywfwtimife

Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks

Sherief Hashima, Basem M. ElHalawany, Kohei Hatano, Kaishun Wu, Ehab Mahmoud Mohamed
2021 Electronics  
A case study of applying multi-armed bandit (MAB) as an efficient online ML tool to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks will be presented.  ...  Device-to-device (D2D) communication is a promising paradigm for the fifth generation (5G) and beyond 5G (B5G) networks.  ...  Author Contributions: All authors contributed equally in this paper. All authors have read and agreed to the published version of the manuscript.  ... 
doi:10.3390/electronics10020169 fatcat:2l764bczknhf7ctlmbsyllllme

A Deep Learning Based Transmission Algorithm for Mobile Device-to-Device Networks

Tae-Won Ban, Woongsup Lee
2019 Electronics  
D2D networks based on deep learning with a convolutional neural network (CNN).  ...  Our numerical results show that the accuracies of the proposed deep learning based transmission algorithm reach about 85%∼95% in spite of its simple structure due to the limitation in computing power.  ...  In addition, the complexity of the supervised deep learning is bounded and predictable compared to deep reinforcement learning (DRL).  ... 
doi:10.3390/electronics8111361 fatcat:iocdf77snraljnkkcrntw3pjdm

Learn to Allocate Resources in Vehicular Networks [article]

Liang Wang, Hao Ye, Le Liang, Geoffrey Ye Li
2019 arXiv   pre-print
In this paper, we exploit deep learning to promote coordination among multiple vehicles and propose a hybrid architecture consisting of centralized decision making and distributed resource sharing to maximize  ...  a deep Q-network to allocate resources and then sends the decision results to all vehicles.  ...  D2D Neural Network Design To fully explore the potentials of V2X networks and make best use of the computational and storage resources at the BS, we devise a new deep reinforcement learning based distributed  ... 
arXiv:1908.03447v1 fatcat:qiak5ysnbzbt7nmr7pv3ac6tjm

Learning to Branch: Accelerating Resource Allocation in Wireless Networks [article]

Mengyuan Lee, Guanding Yu, Geoffrey Ye Li
2019 arXiv   pre-print
Moreover, we develop a mixed training strategy to further reinforce the generalization ability and a deep neural network with a novel loss function to achieve better dynamic control over optimality and  ...  With invariant problem-independent features and appropriate problem-dependent feature selection for D2D communications, a good prune policy can be learned in a supervised manner to speed up the most time-consuming  ...  Power control algorithm in [17] is based on deep reinforcement learning.  ... 
arXiv:1903.01819v2 fatcat:yvmmhikakvd6dmpfortishq5ay
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