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Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets
Abdulmalik Alwarafy
*
, Bekir Sait Ciftler
,
Mohamed Abdallah
,
Mounir Hamdi
, Naofal Al-Dhahir
*
Corresponding author for this work
CSE Information & Computing Technology
HBKU College of Science and Engineering
Hamad bin Khalifa University
University of Texas at Dallas
Research output
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Contribution to journal
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Article
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peer-review
32
Citations (Scopus)
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Dive into the research topics of 'Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets'. Together they form a unique fingerprint.
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Computer Science
Access Technology
100%
Wireless Networks
100%
Dynamic Resource Allocation
100%
Multi-Agent Deep Reinforcement Learning
100%
Power Allocation
50%
System Dynamics
50%
multi agent
25%
Deep Reinforcement Learning
25%
Nonlinear Programming
25%
Learning Agent
25%
Linear Programming Problem
25%
Network Dynamic
25%
Deep Q-Network
25%
Conventional Method
25%
Network Algorithm
25%
sum rate
25%
Engineering
Radio Access Technology
100%
Heterogeneous Network (Communication System)
25%
Mixed-Integer Non-Linear Programming
25%
Sum-Rate Maximization
25%
Main Stage
25%
Optimal Policy
25%
Keyphrases
Device Assignment
33%
Sum-rate Maximization
33%
Cost-aware
33%
Network Utility
33%
Chemical Engineering
Reinforcement Learning
100%