Twin Delayed DRL Approach for Resource Allocation in Multi-User NOMA Systems

Nonorthogonal multiple access (NOMA) technology shows the potential for improving spectral efficiency and enables massive connectivity in future wireless networks. Unlike orthogonal schemes that require separate resources for each user, NOMA allows multiple users to share the same frequency and time...

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Bibliographic Details
Published in:International Conference on Application of Information and Communication Technologies pp. 1 - 5
Main Authors: Rabee, Ayman, Barhumi, Imad
Format: Conference Proceeding
Language:English
Published: IEEE 18.10.2023
Subjects:
deep deterministic policy gradient (DDPG)
deep Q network (DQN)
deep reinforcement learning (DRL)
Energy efficiency
NOMA
Nonorthogonal multiple access (NOMA)
Reinforcement learning
Simulation
Spectral efficiency
Time-frequency analysis
twin-delayed DDPG (TD3)
Wireless networks
ISSN:2472-8586
Online Access:Get full text
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Summary:Nonorthogonal multiple access (NOMA) technology shows the potential for improving spectral efficiency and enables massive connectivity in future wireless networks. Unlike orthogonal schemes that require separate resources for each user, NOMA allows multiple users to share the same frequency and time resource. However, joint subchannel assignment and power allocation in multiuser uplink NOMA systems is NP-hard to solve, posing a significant challenge. In this paper, we formulate this joint problem to maximize the energy efficiency and propose a deep reinforcement learning-based approach as a solution. In this approach, we adopt the twin delayed deep deterministic algorithm for the power allocation and deep Q network for the subchannel assignment. Simulation results demonstrate that the proposed approach improves the energy efficiency performance of the multiuser uplink NOMA system and outperforms other methods.
ISSN:2472-8586
DOI:10.1109/AICT59525.2023.10313195