Explainable AI For Communications N Networking – Hatim Chergui (1)

Gizzini, M. Chafii, A. Nimr, G. Fettweis, Deep learning based channel estima- tion schemes for IEEE 802.11p standard, IEEE Access 8 (2020) 113751–113765. [38] A.K. Gizzini, M. Chafii, 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) 3–14.

[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. 1085–1090. [41] I. Sen, D.W. Matolak, Vehicle–vehicle channel models for the 5-GHz band, IEEE Transactions on Intelligent Transportation Systems 9 (2) (2008) 235–245. [42] A.K. Gizzini, Y. Medjahdi, A.J. Ghandour, L. Clavier, Explainable AI for enhancing efficiency of DL-based channel estimation, arXiv:2407.07009 [cs.AI], 2024. This page intentionally left blank Neuro-symbolic XAI for communications✩ 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.

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 efficient, 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 efficiently handle and interpret vast amounts of data, while providing clear insights into their functioning.

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’ pattern recognition capabilities with symbolic AI’s logical reasoning.

The outcome is a powerful combination, offering high performance and interpretability—indispensable attributes in communication technologies.

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To our beloved ones. This page intentionally left blank Contents List of figures xiii List of tables xv Contributors xvii About the editors xix Preface xxi 1. AI-driven network automation 1 Estefanía Coronado, Blas Gómez, and Gabriel Cebrián-Márquez 1.1. Overview, benefits, and challenges 3 1.1.1. Major benefits 5 1.1.2. Main challenges 6 1.2.

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.

This is a short excerpt from the opening of “” by Unknown, quoted for review and introduction purposes. All rights belong to the copyright holders.

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