Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) a...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2021-09, Vol.20 (9), p.5672-5688
Main Authors: Wang, Shaoyang, Lv, Tiejun, Ni, Wei, Beaulieu, Norman C., Guo, Y. Jay
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Deep learning
deep reinforcement learning (DRL)
Hardware
Hardware sensitivity
imperfect successive interference cancellation (SIC)
Interference
joint resource management (JRM)
Machine learning
multi-carrier non-orthogonal multiple access (MC-NOMA)
Multiagent systems
Multiplexing
Neural networks
NOMA
Nonorthogonal multiple access
Optimization
Resource management
Sensitivity
Silicon carbide
Structural stability
User requirements
Wireless communication
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Summary:This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2021.3069240