{"id":259737,"date":"2026-07-13T17:11:25","date_gmt":"2026-07-13T14:11:25","guid":{"rendered":"https:\/\/1kitap1.com\/en\/explainable-ai-for-communications-n-networking-hatim-chergui-1\/"},"modified":"2026-07-13T17:11:25","modified_gmt":"2026-07-13T14:11:25","slug":"explainable-ai-for-communications-n-networking-hatim-chergui-1","status":"publish","type":"post","link":"https:\/\/1kitap1.com\/en\/explainable-ai-for-communications-n-networking-hatim-chergui-1\/","title":{"rendered":"Explainable AI For Communications N Networking – Hatim Chergui (1)"},"content":{"rendered":"
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Gizzini, M. Cha\ufb01i, A. Nimr, G. Fettweis, Deep learning based channel estima- tion schemes for IEEE 802.11p standard, IEEE Access 8 (2020) 113751\u2013113765. [38] A.K. Gizzini, M. Cha\ufb01i, A. Nimr, G. Fettweis, Joint TRFI and deep learning for vehicular channel estimation, in: IEEE GLOBECOM 2020, Taipei, Taiwan, 2020. [39] J.A. Fernandez, K. Borries, L. Cheng, B.V.K. Vijaya Kumar, D.D. Stancil, Performance of the 802.11p physical layer in vehicle-to-vehicle environments, IEEE Transactions on Vehicular Technology 61 (1) (2012) 3\u201314.<\/p>\n

[40] Y.-K. Kim, J.-M. Oh, Y.-H. Shin, C. Mun, Time and frequency domain channel estimation scheme for IEEE 802.11p, in: 17th International IEEE Conference on In- telligent Transportation Systems (ITSC), 2014, pp. 1085\u20131090. [41] I. Sen, D.W. Matolak, Vehicle\u2013vehicle channel models for the 5-GHz band, IEEE Transactions on Intelligent Transportation Systems 9 (2) (2008) 235\u2013245. [42] A.K. Gizzini, Y. Medjahdi, A.J. Ghandour, L. Clavier, Explainable AI for enhancing ef\ufb01ciency of DL-based channel estimation, arXiv:2407.07009 [cs.AI], 2024. This page intentionally left blank Neuro-symbolic XAI for communications\u2729 Farhad Rezazadeha, Houbing Songb, and Lingjia Liuc aCentre Tecnologic de Telecomunicacions de Catalunya (CTTC), Castelldefels, Spain bUniversity of Maryland, Baltimore County (UMBC), Baltimore, MD, United States cVirginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, United States 5.1.<\/p>\n

Introduction AI in sixth-generation (6G) and beyond communications is a criti- cal step in advancing communication technologies. The AI-based approach transforms how data is processed, transmitted, and received, enabling more ef\ufb01cient, reliable, and transparent networks. This evolution is especially crucial given the characteristics of emerging 6G networks, which are ultra- high speed and low latency. These networks necessitate AI systems that can ef\ufb01ciently handle and interpret vast amounts of data, while providing clear insights into their functioning.<\/p>\n

Among the various AI methodologies, NeSy AI [1] is especially prominent for its explainability, making it excep- tionally suited to meet the complex demands of beyond 6G networks. NeSy AI represents a revolutionary merger of neural network-based methods with symbolic AI principles. This synthesis combines neural net- works\u2019 pattern recognition capabilities with symbolic AI\u2019s logical reasoning.<\/p>\n

The outcome is a powerful combination, offering high performance and interpretability\u2014indispensable attributes in communication technologies.<\/p>\n

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For accessibility purposes, images in electronic versions of this book are accompanied by alt text descriptions provided by Elsevier. For more information, see https:\/\/www.elsevier.com\/about\/ accessibility. Publisher\u2019s note: Elsevier takes a neutral position with respect to territorial disputes or jurisdictional claims in its published content, including in maps and institutional af\ufb01liations. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.<\/p>\n

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As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.<\/p>\n

To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and\/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. ISBN: 978-0-443-29135-7 For information on all Academic Press publications visit our website at https:\/\/www.elsevier.com\/books-and-journals Publisher: Mara Conner Acquisitions Editor: Tim Pitts Editorial Project Manager: Palak Gupta Production Project Manager: Maria Bernard Cover Designer: Christian Bilbow Typeset by VTeX To the souls demystifying intelligence, With letters\u2019 incandescence.<\/p>\n

To our beloved ones. This page intentionally left blank Contents List of \ufb01gures xiii List of tables xv Contributors xvii About the editors xix Preface xxi 1. AI-driven network automation 1 Estefan\u00eda Coronado, Blas G\u00f3mez, and Gabriel Cebri\u00e1n-M\u00e1rquez 1.1. Overview, bene\ufb01ts, and challenges 3 1.1.1. Major bene\ufb01ts 5 1.1.2. Main challenges 6 1.2.<\/p>\n

Use cases 9 1.2.1. Seamless immersive reality 11 1.2.2. Cooperative mobile robots and smart industries 12 1.2.3. Digital twins 12 1.3. Sustainability 13 1.3.1. Carbon telemetry 14 1.3.2. Sustainable AI 15 1.3.3. AI for network sustainability 16 1.4. Related standardization to network automation 17 References 19 2.<\/p>\n<\/blockquote>\n

This is a short excerpt from the opening of “” by Unknown, quoted for review and introduction purposes. All rights belong to the copyright holders.<\/em><\/p>\n

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