IEEE International Mediterranean Conference on Communications and Networking
8–11 July 2024 // Madrid, Spain

WS-05: 1st International Workshop on Explainable and Responsible AI/GenAI for 6G Networks (6GBRAIN)

WS-05: 1st International Workshop on Explainable and Responsible AI/GenAI for 6G Networks (6GBRAIN)


The sixth-generation of wireless networks (6G) transforms the way we interact with technology and promises ultra-fast data transmission, enhanced reliability, and comprehensive connectivity. However, this advancement in network capabilities introduces complex challenges, particularly regarding the role of artificial intelligence (AI) in managing, optimizing, and securing these networks. To streamline the implementation of 6G networks, Generative AI (GenAI) and Large Language Models (LLMs) are viewed as critical technologies to transition from being AI-native to becoming intrinsic automation-native, integrating AI deeply into every aspect of network operations. Following the technical report of the European Commission on "Ethics guidelines for trustworthy AI", AI solutions should pursue trustworthiness. This aligns with the principles that emphasize transparency, fairness, and accountability of AI solutions in 6G Networks. In this context, for AI/GenAI automation to be effectively integrated into 6G commercial networks, it is essential to build substantial trust and clarity in the often opaque "black box" nature of AI. By revealing the impact of different inputs on the outputs, AI developers and 6G researchers can identify and rectify biases, inaccuracies, or unforeseen behaviors in AI models. In this intent, eXplainable AI (XAI) techniques and measurements become crucial. They help clarify the reasoning behind AI's predictions and decisions, which are instrumental in enhancing the comprehension of causality within AI models. The concept of explainability in AI is crucial for 6G to enable the trust and reliability required by critical infrastructures and services. They can be applied in different forms within 6G networks, specifically as Ante-hoc, In-hoc, and Post-hoc explanations.

This workshop aims to bring researchers and scientists together to discuss the opportunities and challenges in the research, design, and engineering of trustworthy and transparent AI/GenAI, especially to enable trust between stakeholders through a deeper understanding of the complexities involved in integrating trustworthy AI/GenAI within 6G networks.

Topics of Interest

We seek original completed and unpublished work not currently under review by any other journal/ magazine/conference. This event will focus on, but will not be limited to, the following subjects of interest:

  • AI/ML-driven 6G Networks
  • Explainable AI/GenAI for 6G
  • AI/GenAI for 6G Network Slicing
  • AI/GenAI for 6G Non-Terrestrial Networks
  • AI/GenAI for Wireless Sensing in 6G
  • AI/GenAI for 6G O-RAN
  • AI/GenAI for energy efficiency in 6G
  • AI/GenAI for security and privacy in 6G
  • AI/GenAI for scalable and massive 6G
  • AI/GenAI for low latency applications
  • GenAI-enhanced semantic communications
  • AI/GenAI-based protocol learning
  • AI/GenAI for users’ behavior analysis and inference
  • GenAI for mobility management and network control
  • AI/GenAI-based resource management
  • AI/GenAI and Blockchain for 6G
  • AI/GenAI integration in 6G PoCs


Keynote 1

Title: Artificial General Intelligence (AGI)-Native Wireless Systems with Common Sense

Biography: Walid Saad (S’07, M’10, SM’15, F’19) received his Ph.D degree from the University of Oslo, Norway in 2010. He is currently a Professor at the Department of Electrical and Computer Engineering at Virginia Tech, where he leads the Network sciEnce, Wireless, and Security (NEWS) laboratory. His research interests include wireless networks (5G/6G/beyond), machine learning, game theory, security, UAVs, semantic communications, cyber-physical systems, and network science. Dr. Saad is a Fellow of the IEEE. He is also the recipient of the NSF CAREER award in 2013, the AFOSR summer faculty fellowship in 2014, and the Young Investigator Award from the Office of Naval Research (ONR) in 2015. He was the (co-)author of twelve conference best paper awards at IEEE WiOpt in 2009, ICIMP in 2010, IEEE WCNC in 2012, IEEE PIMRC in 2015, IEEE SmartGridComm in 2015, EuCNC in 2017, IEEE GLOBECOM (2018 and 2020), IFIP NTMS in 2019, IEEE ICC (2020 and 2022), and IEEE QCE in 2023. He is the recipient of the 2015 and 2022 Fred W. Ellersick Prize from the IEEE Communications Society, of the IEEE Communications Society Marconi Prize Award in 2023, and of the IEEE Communications Society Award for Advances in Communication in 2023. He was also a co-author of the papers that received the IEEE Communications Society Young Author Best Paper award in 2019, 2021, and 2023. He was also an IEEE Distinguished Lecturer in 2019-2020.  He has been annually listed in the Clarivate Web of Science Highly Cited Researcher List since 2019. Dr. Saad is the Editor-in-Chief for the IEEE Transactions on Machine Learning in  Communications and Networking.

Abstract: Next-generation wireless systems, such as 6G and beyond, are expected to tightly embed artificial intelligence (AI) into their design, giving rise to what is termed AI-native wireless systems. Remarkably, despite significant academic, industrial, and standardization efforts dedicated to AI-native wireless systems in the past few years, even the very definition of such systems remains ambiguous. Presently, most endeavors in this domain represent incremental extensions of conventional "AI for wireless" paradigms, employing classical tools like autoencoders, diffusion models, or large-language models to replicate established wireless functionalities. However, such approaches suffer from inherent limitations, including the opaque nature of the adopted AI models, their tendency toward curve-fitting, reliance on extensive training data, energy inefficiency, and limited generalizability to new, unseen scenarios and out-of-domain/out-of-distribution data points. To surmount these challenges, in this talk, we unveil a bold, pioneering framework for the development of artificial general intelligence (AGI)-native wireless systems. We particularly show how the fusion of wireless systems, digital twins, and AI can catalyze a transformative paradigm shift in both wireless and AI technologies by conceptualizing a next-generation AGI architecture imbued with "common sense" capabilities, akin to human cognition and founded on three components: a) perception, b) world model, and c) action-planning. This architecture will empower networks with reasoning, planning, and other human-like cognitive faculties such as imagination and deep thinking. We first define the technical tenets of common sense and, subsequently, we demonstrate how the proposed AGI architecture can instill a new level of generalizability, explainability, and reasoning into tomorrow’s wireless networks.  We then discuss how AGI-native wireless systems can unleash novel use cases such as digital twins with analogical reasoning, resilient experiences for cognitive avatars, and brain-level holographic experiences. Following the establishment of the foundational principles and components of AGI-native wireless systems, we take a significant stride forward by forging a link with the emerging concept of semantic communications. In doing so, we demonstrate how the integration of causal reasoning (a key component of our AGI vision) with semantic communication can usher in a new era of knowledge-driven, reasoning-capable AGI-native wireless systems. These systems represent a major departure from today’s data-driven, knowledge-agnostic models, offering enhanced sustainability and resilience in their design and operation. We present our recent key results, rooted in AI, theory of mind, digital twins, and game theory, laying the groundwork for the realization of AGI-native wireless systems, and illustrating how our designed framework reduces data volume in networks while enhancing reliability, crucial for next-generation wireless services like connected intelligence and holography. We conclude with a discussion on the exciting opportunities in this field that can help redefine the intersection of wireless communications and AI.


Keynote 2

Title: Neuro-symbolic AI: The Third Wave of AI

Biography: Houbing Herbert Song (IEEE Fellow) received the M.S. degree in civil engineering from The University of Texas at El Paso, El Paso, TX, USA, in December 2006, and the Ph.D. degree in electrical engineering from the University of Virginia, Charlottesville, VA, USA, in August 2012. He has been serving as an Associate Technical Editor for IEEE Communications Magazine since 2017, an Associate Editor for IEEE Internet of Things Journal since 2020, IEEE Transactions on Intelligent Transportation Systems since 2021, and IEEE Journal on Miniaturization for Air and Space Systems since 2020, and the Guest Editor for IEEE Journal on Selected Areas in Communications. He is an ACM Distinguished Member and an ACM Distinguished Speaker. He is a Highly Cited Researcher identified by Clarivate™ (2021, 2022) and a Top 1000 Computer Scientist identified by He received the Rising Star of Science Award in 2022 (World Ranking: 82; USA Ranking: 16). He was a recipient of more than best paper awards from major international conferences, including the IEEE CPSCom-2019, the IEEE ICII 2019, the IEEE/AIAA ICNS 2019, the IEEE CBDCom 2020, the WASA 2020, the AIAA/IEEE DASC 2021, the IEEE GLOBECOM 2021, and the IEEE INFOCOM 2022.

Abstract: The past few years has witnessed tremendous successes in applications of AI, particularly data-driven machine learning (ML). However, there are still several major limitations of state-of-the-art (SOTA) AI algorithms: generalizability, interpretability, and robustness. An emerging subfield of AI, neuro-symbolic AI (also called “The Third Wave of AI”), has the potential to overcome these limitations through the integration of symbolic representations with neural networks. In this talk, I will share my journey from internet of things to incremental learning and transfer learning to neuro-symbolic AI; then I will present my perspective on the emerging area with a focus on neuro-symbolic transfer learning, neuro-symbolic reinforcement learning, and neuro-symbolic AI testing and evaluation.


Important Dates

Paper Submission Deadline:  5 April 2024 30 April 2024 (FIRM)
Paper Acceptance Notification:  14 May 2024
Camera Ready:  1 June 2024


Submission Link



Farhad Rezazadeh, CTTC, Spain (Contact: farhad.rh[at]ieee[dot]org)
Hatim Chergui, i2CAT Foundation, Spain
Nguyen Van Huynh, University of Liverpool, United Kingdom
Yongxin Liu, Embry-Riddle Aeronautical University, United States
Toktam Mahmoodi, King's College London, United Kingdom
Dusit Niyato, Nanyang Technological University, Singapore


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