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Related Experiment Video

Updated: May 17, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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Channel and Power Allocation for Multi-Cell NOMA Using Multi-Agent Deep Reinforcement Learning and Unsupervised

Ming Sun1, Yihe Zhong1, Xiaoou He1

  • 1College of Computer and Control Engineering, Qiqihar University, Qiqihar 161006, China.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
Summary

This study introduces a novel Multi-Agent Deep Reinforcement Learning with Unsupervised Learning (MDRL-UL) framework to optimize channel and power allocation in non-orthogonal multiple access (NOMA) systems, significantly boosting energy efficiency and data rates.

Keywords:
attention mechanismchannel allocationmulti-agent deep reinforcement learningnon-orthogonal multiple access (NOMA)power allocationunsupervised learning

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Area of Science:

  • Wireless communication systems
  • Telecommunications engineering
  • Artificial intelligence in networking

Background:

  • Non-orthogonal multiple access (NOMA) offers enhanced data throughput for 5G and 6G technologies.
  • Optimizing channel and power allocation in multi-cell NOMA systems is complex due to dynamic environments and lack of optimal labels.
  • Conventional supervised learning methods are insufficient for NOMA resource allocation.

Purpose of the Study:

  • To propose an efficient framework for near-optimal channel and power resource allocation in multi-cell NOMA systems.
  • To enhance the energy efficiency and transmission rates of NOMA systems.
  • To address the limitations of supervised learning in dynamic wireless environments.

Main Methods:

  • Integration of Multi-Agent Deep Reinforcement Learning (MDRL) for channel allocation and Unsupervised Learning (UL) for power allocation.
  • Development of a Multi-Agent Deep Reinforcement Learning Neural Network (MDRLNN) for channel allocation.
  • Utilization of an attention-based Unsupervised Learning Neural Network (ULNN) for power allocation, using joint actions from MDRLNN as input.
  • Training both MDRLNN and ULNN using the expectation of energy efficiency.

Main Results:

  • The proposed MDRL-UL framework achieves near-optimal channel and power allocation.
  • Simulation results demonstrate superior performance compared to existing algorithms.
  • The system exhibits significantly higher energy efficiency and improved transmission rates.

Conclusions:

  • The MDRL-UL framework effectively overcomes challenges in NOMA resource allocation.
  • This approach offers a promising solution for maximizing performance in advanced wireless systems.
  • The integration of MDRL and UL provides a robust method for dynamic resource management.