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Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning
Chongli Zhang, Tiejun Lv, Pingmu Huang, Zhipeng Lin
*
,
Jie Zeng
, Yuan Ren
*
此作品的通讯作者
网络空间安全学院
Beijing University of Posts and Telecommunications
Nanjing University of Aeronautics and Astronautics
Xi'an Institute of Posts and Telecommunications
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探究 'Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning' 的科研主题。它们共同构成独一无二的指纹。
分类
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按字母排序
Engineering
Joints (Structural Components)
100%
Reinforcement Learning
100%
Subproblem
100%
Power Allocation
100%
Resource Utilisation
50%
Multiuser
25%
Genetic Algorithm
25%
Learning Approach
25%
Cell Multi
25%
Quality of Service
25%
Channel Interference
25%
Cell Interference
25%
Computer Science
Deep Reinforcement Learning
100%
Power Allocation
100%
multi agent
50%
Resource Utilisation
50%
Reinforcement Learning
25%
Learning Approach
25%
Genetic Algorithm
25%
Quality of Service
25%
Utilization Rate
25%
Training Model
25%
Quality Problem
25%
Channel Interference
25%
Deep Q-Network
25%
intercell interference
25%
Reinforcement Learning-Based Method
25%
Bandwidth Allocation
25%