Enhancing Energy Efficiency and Data Rate in MIMO-NOMA Systems Based on Communication Deep Neural Networks for 6G Communications

R. Poornima, S. Jayachitra, Ajay A., Dhivakar S., Indra Kumar U., Jayasakthi S P

2025

Abstract

The emergence of 6G communication networks requires novel techniques to deliver massive connectivity with high data rate, and enhanced energy efficiency, driving the future of communication systems. The integration of Non-Orthogonal Multiple Access (NOMA) with Multiple-Input Multiple-Output (MIMO) systems, offers a promising solution to enhance system energy efficiency and data rate. Rapidly changing channel conditions and complex spatial structures degrade system performance and limit its applicability. To address these restrictions, this article proposes a deep learning-based MIMO-NOMA framework that maximizes data rate and energy efficiency. Specifically, we develop a novel Communication Deep Neural Network (CDNN) architecture comprising multiple hidden layers and convolution layers. The deep learning techniques such as, the CDNN framework uses training algorithms to solve the power allocation problem and increase MIMO-NOMA's energy efficiency and data rate. Furthermore, simulation results demonstrate that the suggested CDNN framework has better data rate and energy efficiency than the Secondary BS-aided scheme, ᾳ- fairness aided based scheme, LSTM-NOMA based scheme and basic deep learning scheme. The Secondary BS-aided scheme data rate mean is 2.4586, fairness aided based scheme mean is 2.4986, LSTM-NOMA based scheme mean is 2.5343, deep learning scheme mean is 2.6571 and proposed CDNN scheme data rate mean is 2.8514. So that proposed CDNN framework has higher energy efficiency and data rate than compared to other regular methodologies.

Paper Citation

in Harvard Style

Poornima R., Jayachitra S., A. A., S. D., U. I. and S P J. (2025). Enhancing Energy Efficiency and Data Rate in MIMO-NOMA Systems Based on Communication Deep Neural Networks for 6G Communications. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 600-607. DOI: 10.5220/0013887100004919

in Bibtex Style

@conference{icrdicct`2525,
author={R. Poornima and S. Jayachitra and Ajay A. and Dhivakar S. and Indra U. and Jayasakthi S P},
title={Enhancing Energy Efficiency and Data Rate in MIMO-NOMA Systems Based on Communication Deep Neural Networks for 6G Communications},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={600-607},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013887100004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Enhancing Energy Efficiency and Data Rate in MIMO-NOMA Systems Based on Communication Deep Neural Networks for 6G Communications
SN - 978-989-758-777-1
AU - Poornima R.
AU - Jayachitra S.
AU - A. A.
AU - S. D.
AU - U. I.
AU - S P J.
PY - 2025
SP - 600
EP - 607
DO - 10.5220/0013887100004919
PB - SciTePress