Energy Efficient Resource Allocation Approach for Uplink NOMA Multi-Cell Systems Based on Multi-Agent DRL

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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC pp. 1 - 6
Main Authors: Rabee, Ayman, Barhumi, Imad
Format: Conference Proceeding
Language:English
Published: IEEE 21.04.2024
Subjects:
Benchmark testing
deep Q network (DQN)
deep reinforcement learning (DRL)
multi-agent twin-delayed DDPG (MATD3)
NOMA
Nonorthogonal multiple access (NOMA)
Reinforcement learning
Simulation
Spectral efficiency
Time-frequency analysis
Wireless networks
ISSN:1558-2612
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
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 cell association, subchannel assignment, and power allocation problem in uplink multi-cell NOMA systems is NP-hard to solve, posing a significant challenge. In this paper, we formulate this joint problem to maximize energy efficiency and propose a multi-agent deep reinforcement learning-based approach as a solution. In this approach, we adopt the multi-agent twin delayed deep deterministic algorithm (MATD3) for the power allocation and deep Q network for the cell association and subchannel assignment. Simulation results demonstrate that the proposed approach improves the energy efficiency performance of the uplink multi-cell NOMA system and outperforms other methods.
ISSN:1558-2612
DOI:10.1109/WCNC57260.2024.10571217