ItemBook Chapter

Non-orthogonal multiple access wireless systems using deep learning

Title
Non-orthogonal multiple access wireless systems using deep learning
Creator
Gohil, Rudraksh; Deepa, S.
Description
In 5G networks, non-orthogonal multiple access (NOMA) increases spectral efficiency and user capacity greatly by letting multiple users share the same time, frequency, and code resources. Wireless communication systems stand to benefit significantly from deep learning owing to its ability to model intricate patterns. This chapter centers around deep learning-NOMA integration with special attention given to areas like channel estimation, interference management, and dynamic resource allocation. Using advanced deep learning frameworks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and deep reinforcement learning (DRL), this chapter demonstrates how NOMA system performance can be optimized to meet the stringent requirements of 5G and beyond networks. Moreover, this chapter also discusses the challenges associated with implementing deep learning in NOMA including computational complexity and data requirements, alongside future trends like federated learning and edge computing among others. The integration of these technologies promises improved network efficiency, reduced latency, and enhanced user experience, thereby making NOMA a fundamental technology in wireless communication evolution. 2025 selection and editorial matter, Mariyam Ouaissa, Mariya Ouaissa, Hanane Lamaazi, Khadija Slimani, Ihtiram Raza Khan, and B. Sundaravadivazhagan.
Source
Machine Learning for Radio Resource Management and Optimization in 5G and Beyond;pp.95-115
Date
01-01-2025
Publisher
CRC Press
Coverage
Gohil R., Christ University, Bangalore, India; Deepa S., Christ University, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-104032761-6; 978-103284473-2;
Format
online
Language
English
Type
Book chapter

Citation

Gohil, Rudraksh; Deepa, S., “Non-orthogonal multiple access wireless systems using deep learning,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/24360.

Output Formats