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 fra...
The emerging 3GPP 6G RAN architecture with emphasis on boundary selection is reviewed, which 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 interf...
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...
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.
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