Explainable AI for Communications and Networking

Toward Responsible Automation

1st Edition - April 8, 2025
Editors: Hatim Chergui, Melike Erol-Kantarci, Christos Verikoukis
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 2 9 1 3 5 - 7
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 9 1 3 6 - 4

Explainable AI for Communications and Networking: Toward Responsible Automation gives a tour into the realm of Explainable Artificial Intelligence (XAI) and its impact on transp… Read more

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Explainable AI for Communications and Networking: Toward Responsible Automation gives a tour into the realm of Explainable Artificial Intelligence (XAI) and its impact on transparent and autonomous communication networks. The book equips readers from diverse backgrounds in communications and networking with a variety of XAI tools, metrics and frameworks to demystify AI systems through graphical taxonomies and mathematical formulations, which are further enriched with code snippets. The book also examines XAI implementation in wireless communications, network management, generative AI for telecom and cybersecurity, before presenting practical use-cases emanating from an industry perspective. Finally, the regulatory, ethical, and legal implications of XAI in telecommunications are reviewed, before concluding with key challenges and takeaways.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Dedication
List of figures
List of tables
Contributors
About the editors
Preface
Chapter One: AI-driven network automation
1.1. Overview, benefits, and challenges
1.1.1. Major benefits
1.1.2. Main challenges
1.2. Use cases
1.2.1. Seamless immersive reality
1.2.2. Cooperative mobile robots and smart industries
1.2.3. Digital twins
1.3. Sustainability
1.3.1. Carbon telemetry
1.3.2. Sustainable AI
1.3.3. AI for network sustainability
1.4. Related standardization to network automation
Chapter Two: Explainable AI overview
2.1. Why transparency in AI?
2.2. Concepts and definitions
2.3. Taxonomy of XAI techniques with examples
2.3.1. Model transparency
2.3.2. Model agnosticity
2.3.3. Scope
2.3.4. Explanation basis
2.4. XAI methods
2.4.1. SHAP
2.4.2. Integrated gradient (IG)
2.4.3. Gradient × input
2.4.4. Saliency maps
2.4.5. LRP
2.4.6. DeepLIFT
2.4.7. LIME
2.4.8. Rulefit
2.4.9. Counterfactual
2.4.10. GNN explainer
2.4.11. Reward shaping
2.4.12. XRL
2.4.13. Attention flow analysis
2.4.14. Machine reasoning (MR)
2.4.15. Structural causal models (SCM)
2.4.16. Caption generation
2.4.17. Knowledge graphs
2.4.18. Explanation-guided learning
2.5. Ranking of XAI methods in O-RAN prediction tasks
2.6. Key considerations in implementing XAI
Annex: python code for generating results of Fig. 2.4
Chapter Three: XAI metrics and frameworks
3.1. Introduction
3.2. Need for evaluation framework for XAI mechanisms
3.3. Metric ingredients
3.4. XAI metrics
3.4.1. Confidence/faithfulness
3.4.2. Log-odds
3.4.3. Comprehensiveness
3.4.4. Sufficiency
3.4.5. Robustness/sensitivity
3.4.6. Ambiguity
3.4.7. Infidelity
3.4.8. Relative consistency
3.4.9. Evaluation metrics for large language models in explainable AI
3.4.10. Hallucination semantic entropy
3.5. Application of XAI evaluation approaches
3.5.1. Human-centered evaluation for XAI mechanisms
3.5.2. Case study 1: PoC for explanation analysis using XAI metrics and properties
3.5.3. Case study 2: Evaluation of some XAI metrics in a DNN-based CPU prediction task for o-RAN
3.5.4. Case study 3: in-hoc federated learning confidence
3.5.5. Case study 4: Metrics-enabled XAI pipelines
3.6. XAI frameworks: Python libraries
3.6.1. DeepExplain
3.6.2. LIME (Local Interpretable Model-agnostic Explanations)
3.6.3. SHAP (SHapley Additive exPlanations)
3.6.4. ELI5 (Explain Like I am 5)
3.6.5. Captum
3.6.6. AI explainability 360 (AIX360)
3.6.7. Alibi
3.6.8. DALEX (Descriptive mAchine Learning EXplanations)
Annex: Python Code for generating results of Table 3.1
Chapter Four: XAI for wireless communications
4.1. Introduction
4.2. Toward XAI-based solutions
4.3. Signal model
4.4. FNN-based channel estimation schemes
4.4.1. STA-FNN
4.4.2. TRFI-FNN
4.5. XAI for channel estimation
4.6. Performance evaluation
4.6.1. Simulation setup
4.6.2. BER performance analysis
4.7. Conclusion
Chapter Five: Neuro-symbolic XAI for communications
5.1. Introduction
5.2. Neuro-symbolic AI
5.2.1. Learning for reasoning model
5.2.2. Reasoning for learning model
5.2.3. Learning–reasoning model
5.3. Reinforcement learning
5.4. A practical guide to neuro-symbolic integration in RL
5.4.1. Complex symbolic rules
5.4.2. Symbolic state representation
5.4.3. Symbolic reward shaping
5.4.4. Symbolic policy constraints
5.4.5. Logical inference integration
5.4.6. Hybrid state-action space
5.5. Advancing from neuro-symbolic AI to XAI
5.6. Neuro-symbolic XAI opportunities in 6G networks
5.6.1. Enhanced data processing and network optimization
5.6.2. Security and privacy
5.6.3. User experience personalization
5.6.4. Sustainable network operations
5.7. Neuro-symbolic XAI challenges in 6G networks
5.7.1. Complexity of integration
5.7.2. Scalability issues
5.7.3. Data quality and availability
5.8. Conclusion
Chapter Six: XAI for network management
6.1. Introduction
6.2. Motivation
6.3. AIChronoLens.
6.3.1. The design
6.3.2. The rationale of explanations
6.4. Use cases of AIChronoLens.
6.4.1. Evaluation settings
6.4.2. Error analysis
6.4.3. Model benchmarking
6.5. Discussion and concluding remarks
6.5.1. Applications of AI/ML to network management
6.5.2. Concluding remarks
Chapter Seven: Responsible generative AI for telecom
7.1. Multimodal generative AI for telecom
7.1.1. Intent-based networking
7.1.2. Log anomaly detection
7.1.3. Multimodal self-network optimization
7.2. Generative AI vulnerabilities
7.3. Use case: interpreting emerging communication via GenAI
7.3.1. Framework setup
7.3.2. Evaluation
7.4. Conclusions
Annex: GPT-neo fine-tuning and perplexity evaluation code
Chapter Eight: Explainable AI for cybersecurity decisions: challenges and opportunities
8.1. Introduction and motivation
8.2. XAI for cybersecurity: objectives, positioning, and challenges
8.3. Human-oriented explanations to augment SOC operations
8.3.1. Preliminaries
8.3.2. Computing candidate explanations
8.3.3. Generating the GR catalog
8.3.4. Alignment of production data with the ground truth catalog
8.4. Limitation and current research challenges
8.5. Conclusions
Chapter Nine: Explainable AI for telecom use cases
9.1. Introduction
9.2. Remote electrical tilt optimization use case using eXplainable reinforcement learning
9.3. Proactive assurance of network slices using eXplainable machine learning, XAI-quantification, and counterfactuals
9.4. Explainable RL in collaborative robotics use case for industry 4.0
9.5. Auto-node provisioning for recommending configuration
9.6. Enhanced decision support in 6G BRICKS EU project
9.7. Counterfactual analysis & recourse action for energy savings in telecom networks
Chapter Ten: XAI: regulatory and ethical aspects
10.1. The regulatory landscape for explainable AI
10.1.1. Governmental approaches
10.1.2. Standardization efforts for integrating explainability
10.2. Ethical theories, applications, and risks in XAI
10.2.1. Major ethical theories
10.2.2. Contextual ethical risks in XAI
10.3. Conclusion
Chapter Eleven: Future directions and challenges
11.1. Performance–explainability trade-off
11.2. GenAI for telecom: an explainability perspective
11.3. Lack of standardization
11.4. Interoperable XAI models for the complex multi-vendor 6G networks
11.5. Complexity of the 6G networks systems
11.6. Real-time constraints
11.7. Heterogeneity of target audiences in XAI
11.8. Privacy–explainability trade-off
11.9. The two facets of XAI and security
11.10. Enhancing XAI methods
Chapter Twelve: Conclusions: a human-centric perspective
Index

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Explainable AI for Communications and Networking

Toward Responsible Automation

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Hatim Chergui

Melike Erol-Kantarci

Christos Verikoukis

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