Performance Analysis of Cooperative Non-Orthogonal Multiple Access System Using Deep Learning Technique

M Ramadevi, Motla Kundhan Reddy, Chidurala Saiteja, Tummala Deepak Raja

2025

Abstract

Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technique to enhance spectral efficiency in wireless communication systems. It allows users with varying channel conditions to share the same frequency band, enabling simultaneous transmission. Cooperative NOMA, an extension of NOMA, further elevates system performance by leveraging cooperative communication and exploiting spatial diversity. This study proposes a novel approach for evaluating the performance of cooperative NOMA systems utilizing deep learning techniques, specifically Convolutional Neural Networks (CNNs). Unlike conventional analytical methods, deep learning models excel in capturing intricate patterns and correlations in large-scale wireless communication systems, rendering them well-suited for performance analysis tasks. The suggested CNN-based framework is trained using simulated data derived from a cooperative NOMA system model. Inputs to the CNN encompass channel state information, power allocation parameters, and other pertinent system parameters, while the output entails achievable Outage Probability or Bit Error Rate predictions. By discerning the relationship between system parameters and performance metrics, the CNN adeptly forecasts the cooperative NOMA system's performance across diverse scenarios. The effectiveness of the proposed approach is assessed through extensive simulations, wherein the performance of the CNN-based model is juxtaposed against traditional analytical methods. Results indicate the CNN's superiority in terms of accuracy and computational efficiency, particularly in scenarios characterized by complex channel conditions and dynamic network environments. In summary, this study underscores the potential of deep learning techniques, particularly CNNs, in scrutinizing and optimizing cooperative NOMA systems. This paves the way for the streamlined design and deployment of next- generation wireless communication networks, promising enhanced efficiency and performance.

Paper Citation

in Harvard Style

Ramadevi M., Reddy M., Saiteja C. and Raja T. (2025). Performance Analysis of Cooperative Non-Orthogonal Multiple Access System Using Deep Learning Technique. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 908-913. DOI: 10.5220/0013734800004664

in Bibtex Style

@conference{incoft25,
author={M Ramadevi and Motla Kundhan Reddy and Chidurala Saiteja and Tummala Deepak Raja},
title={Performance Analysis of Cooperative Non-Orthogonal Multiple Access System Using Deep Learning Technique},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={908-913},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013734800004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Performance Analysis of Cooperative Non-Orthogonal Multiple Access System Using Deep Learning Technique
SN - 978-989-758-763-4
AU - Ramadevi M.
AU - Reddy M.
AU - Saiteja C.
AU - Raja T.
PY - 2025
SP - 908
EP - 913
DO - 10.5220/0013734800004664
PB - SciTePress