Mohammadreza Mosahebfard

arXiv2026

AI-Native Orchestration in the 6G Continuum: Evolving Operator Platforms with Agentic AI

As Sixth-Generation (6G) networks evolve towards a seamless Cloud-Edge-Internet of Things (IoT) continuum, autonomous orchestration across distributed compute and network domains becomes critical. Future 6G services will span multiple administrative and operator domains, making federation essential for ubiquitous, ultra-low-latency service continuity beyond individual footprints. This complexity demands AI-native mechanisms supporting intent-driven automation and closed-loop management. While the GSMA Operator Platform (OP) provides the architectural blueprint for multi-operator federation and network capability exposure, and the ETSI Software Development Group OpenOP (SDG OOP) offers a primary open-source reference implementation, current frameworks are limited by stateless API interactions and lack native intelligence. This paper proposes an Agentic-driven Intelligence extension for the GSMA OP architecture, using the OOP as the reference framework. We introduce an AI-native orchestration layer where autonomous agents manage persistent service contexts and enable closed-loop control via CAMARA APIs. By integrating a Declarative Monitoring and Alerting System (DeMAS) into the OOP stack and establishing a decentralised agent negotiation protocol, the proposed architecture enables real-time, intent-driven resource optimisation and autonomous cross-domain conflict resolution across federated domains. We validate our approach through a representative 6G use case involving Ultra-Reliable Low-Latency Communication (URLLC) and enhanced Mobile Broadband (eMBB) coexistence, demonstrating that an agentic OP framework autonomously reconciles stringent Service Level Agreements (SLAs) while enhancing infrastructure energy efficiency. Our findings establish a scalable blueprint for cross-domain Network-as-a-Service (NaaS) models that align standardised exposure with 6G autonomous requirements.

cs.NI↗

arXiv2022

Towards Quantum-Enabled 6G Slicing

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-generation (6G), where the mobile communication systems are underpinned in the form of advanced tenancy-based digital use-cases to meet different service requirements. To overcome the challenges of massive slices such as handling the increased dynamism, heterogeneity, amount of data, extended training time, and variety of security levels for slice instances, the power of quantum computing pursuing a distributed computation and learning can be deemed as a promising prerequisite. In this intent, we propose a cloud-native federated learning framework based on quantum deep reinforcement learning (QDRL) where distributed decision agents deployed as micro-services at the edge and cloud through Kubernetes infrastructure then are connected dynamically to the radio access network (RAN). Specifically, the decision agents leverage the remold of classical deep reinforcement learning (DRL) algorithm into variational quantum circuits (VQCs) to obtain the optimal cooperative control on slice resources. The initial numerical results show that the proposed federated QDRL (FQDRL) scheme provides comparable performance than benchmark solutions and reveals the quantum advantage in parameter reduction. To the best of our knowledge, this is the first exploratory study considering an FQDRL approach for 6G communication network.

cs.NI↗