TL;DR
CIS-RAN is proposed, a novel 6G RAN architecture built on three pillars of cooperative, intelligent, and service-based design that enables flexible collaboration across distributed RAN nodes and multi-dimensional domains, integrates intelligence throughout the network, and supports advanced RAN capability exposure as services.
Fifth-generation (5G) networks and the Internet of Things (IoT) demand unprecedented levels of scalability and ultra-low latency. Addressing these needs requires not only advanced radio technologies but also a cohesive integration of diverse architectural standards. In this paper, we present a unified fog computing framework inspired by ETSI and OneM2M technical literature that merges the Open Radio Access Network (O-RAN) architecture, Multi-Access Edge Computing (MEC), and the OneM2M IoT standard. This approach enables real-time resource allocation, reduces end-to-end latency, alleviates network congestion, and streamlines interoperability across heterogeneous deployments. Through a simulated testbed, we demonstrate how MEC and OneM2M elements can use near-RT RIC intel to optimize service delivery. The results highlight the feasibility and potential performance gains of an integrated O-RAN-MEC-OneM2M environment, paving the way for more robust, scalable, and efficient 5G IoT solutions.
The 6G radio access network (RAN) architecture is emerging as a disciplined evolution of 5G RAN. The 5G baseline introduced modular base station, providing a flexible framework for diverse deployment scenarios and multi-vendor interoperability. The key architectural challenge for 6G RAN is to preserve these benefits while adapting the RAN to new deployment and service requirements. This article reviews the emerging 3GPP 6G RAN architecture with emphasis on boundary selection. It discusses central unit and distributed unit split enhancements, recognition of radio unit as a distinct logical unit, and the RAN-core network interface study, where point-to-point signaling is favored over service-based interface for connectivity services. It also highlights open areas including RAN-core network interface for non-connectivity services, data collection framework, and artificial intelligence for 6G RAN.
Intent-Based Networking (IBN) has emerged as a promising paradigm for simplifying network management by allowing operators and applications to specify high-level service objectives rather than low-level device configurations. Early IBN research was mainly developed in Software-Defined Networking (SDN), Network Function Virtualization (NFV), transport networks, core networks, and data-center environments, where programmability, virtualization, and relatively stable infrastructure models enabled intent translation, orchestration, and assurance. However, realizing IBN in end-to-end mobile networks is more challenging because the Radio Access Network (RAN) is highly dynamic, wireless-channeldependent, mobility-sensitive, and governed by multiple control timescales. The emergence of Open RAN (O-RAN) changes this landscape by making the RAN programmable, disaggregated, data-driven, and control-lable through non-real-time and near-real-time intelligent control loops. This survey reviews the evolution of IBN from SDN/NFV-enabled automation toward O-RAN-driven end-to-end intent-based networking for 5G-Advanced and 6G. We discuss architectural mechanisms, key challenges, recent advances in AI-driven and agentic IBN, and future research directions including Large Language Model (LLM)-based intent translation, contractbased O-RAN slicing, digital twin-assisted validation, and trustworthy closed-loop orchestration.
This survey formally categorizes state-of-the-art DTN architectures into passive monitoring twins and active control twins, and provides an in-depth evaluation of their underlying enabling technologies, specifically ray-tracing, reconfigurable intelligent surfaces, artificial intelligence, and mobile edge computing.
This dissertation focuses on the design, optimization, and experimental evaluation of NextG RANs integrating Open RAN principles, data-driven control loops, and intelligent resource allocation, demonstrating NextG RANs reconfigurable in real time to meet 6G requirements.
A comprehensive and structured review of methods for MACNs, with particular emphasis on AI-driven solutions and their relationship to classical and hybrid alternatives, and offers insights into the design of AI-driven MACNs that are efficient, scalable, and adaptive to evolving network and service demands.