Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Gohil, Rudraksh; Deepa, S. Title A name given to the resource Non-orthogonal multiple access wireless systems using deep learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Machine Learning for Radio Resource Management and Optimization in 5G and Beyond;pp.95-115 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003514336-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003514336-6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85218655745?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218655745?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Gohil R., Christ University, Bangalore, India; Deepa S., Christ University, India Description An account of the resource 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. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104032761-6; 978-103284473-2; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online