12 citations · 28 across the 15 of their papers we have counts for
15 papers
Mitigating Anchoring Bias in LLM-Based Agents for Energy-Efficient 6G Autonomous Networks
Hatim Chergui, Claudia Carballo González, Farhad Rezazadeh +1
This paper presents an autonomous agentic resource negotiation framework designed to enable zero-touch network slicing in 6G architectures using Large Language Model (LLM) agents.…
LLM-Based Agentic Negotiation for 6G: Addressing Uncertainty Neglect and Tail-Event Risk
Hatim Chergui, Farhad Rezazadeh, Mehdi Bennis +2
A critical barrier to the trustworthiness of sixth-generation (6G) agentic autonomous networks is the uncertainty neglect bias; a cognitive tendency for large language model (LLM)-…
A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks
Hatim Chergui, Farhad Rezazadeh, Merouane Debbah +1
The path to higher network autonomy in 6G lies beyond the mere optimization of key performance indicators (KPIs), requiring systems that perceive and reason over the network enviro…
Towards Cloud-Native Agentic Protocol Learning for Conflict-Free 6G: A Case Study on Inter-Slice Resource Allocation
Juan Sebastián Camargo, Farhad Rezazadeh, Hatim Chergui +2
In this paper, we propose a novel cloud-native architecture for collaborative agentic network slicing. Our approach addresses the challenge of managing shared infrastructure, parti…
Joint Explainability and Sensitivity-Aware Federated Deep Learning for Transparent 6G RAN Slicing
Swastika Roy, Farhad Rezazadeh, Hatim Chergui +1
In recent years, wireless networks are evolving complex, which upsurges the use of zero-touch artificial intelligence (AI)-driven network automation within the telecommunication in…
SliceOps: Explainable MLOps for Streamlined Automation-Native 6G Networks
Farhad Rezazadeh, Hatim Chergui, Luis Alonso +1
Sixth-generation (6G) network slicing is the backbone of future communications systems. It inaugurates the era of extreme ultra-reliable and low-latency communication (xURLLC) and…
A Multi-Agent Deep Reinforcement Learning Approach for RAN Resource Allocation in O-RAN
Farhad Rezazadeh, Lanfranco Zanzi, Francesco Devoti +4
Artificial intelligence (AI) and Machine Learning (ML) are considered as key enablers for realizing the full potential of fifth-generation (5G) and beyond mobile networks, particul…
Explanation-Guided Deep Reinforcement Learning for Trustworthy 6G RAN Slicing
Farhad Rezazadeh, Hatim Chergui, Josep Mangues-Bafalluy
The complexity of emerging sixth-generation (6G) wireless networks has sparked an upsurge in adopting artificial intelligence (AI) to underpin the challenges in network management…
Towards Quantum-Enabled 6G Slicing
Farhad Rezazadeh, Sarang Kahvazadeh, Mohammadreza Mosahebfard
The quantum machine learning (QML) paradigms and their synergies with network slicing can be envisioned to be a disruptive technology on the cusp of entering to era of sixth-genera…
A Federated DRL Approach for Smart Micro-Grid Energy Control with Distributed Energy Resources
Farhad Rezazadeh, Nikolaos Bartzoudis
The prevalence of the Internet of things (IoT) and smart meters devices in smart grids is providing key support for measuring and analyzing the power consumption patterns. This app…
On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration
Farhad Rezazadeh, Lanfranco Zanzi, Francesco Devoti +3
Network slicing enables multiple virtual networks to be instantiated and customized to meet heterogeneous use case requirements over 5G and beyond network deployments. However, mos…
A Collaborative Statistical Actor-Critic Learning Approach for 6G Network Slicing Control
Farhad Rezazadeh, Hatim Chergui, Luis Blanco +2
Artificial intelligence (AI)-driven zero-touch massive network slicing is envisioned to be a disruptive technology in beyond 5G (B5G)/6G, where tenancy would be extended to the fin…
Actor-Critic-Based Learning for Zero-touch Joint Resource and Energy Control in Network Slicing
Farhad Rezazadeh, Hatim Chergui, Loizos Christofi +1
To harness the full potential of beyond 5G (B5G) communication systems, zero-touch network slicing (NS) is viewed as a promising fully-automated management and orchestration (MANO)…
Zero-touch Continuous Network Slicing Control via Scalable Actor-Critic Learning
Farhad Rezazadeh, Hatim Chergui, Christos Verikoukis
Artificial intelligence (AI)-driven zero-touch network slicing is envisaged as a promising cutting-edge technology to harness the full potential of heterogeneous 5G and beyond 5G (…
Continuous Multi-objective Zero-touch Network Slicing via Twin Delayed DDPG and OpenAI Gym
Farhad Rezazadeh, Hatim Chergui, Luis Alonso +1
Artificial intelligence (AI)-driven zero-touch network slicing (NS) is a new paradigm enabling the automation of resource management and orchestration (MANO) in multi-tenant beyond…