A Space-Intelligence Integrated Communication Architecture for Intent-Driven Network Reconfiguration: Focusing on the Velsanet Model

[Paper Outline] Spatial Structure + Intent-Based Processing + AI-Driven Network Reconfiguration

Title: "A Space-Intelligence Integrated Communication Architecture for Intent-Driven Network Reconfiguration: Focusing on the Velsanet Model"

1. Introduction

Modern communication infrastructures are based on static address-dependent connections and predefined paths, making them inflexible to dynamic changes. This paper theoretically proposes a new model combining "spatial structure + intent-based processing + AI-driven reconfiguration," arguing that the Velsanet network model offers a transformative architecture for the future of intelligent network systems.

2. Background and Objective

• Traditional networks have fixed connections between nodes and cannot interpret intent.
• AI can act as a processing entity, but it remains separate from the physical network structure.
• This research proposes a unified architecture that integrates the following three components:

3. Core Theoretical Models

3.1 Polyhedral-Based Spatial Structure

• Regular polyhedra provide a face-level structure with vertical, horizontal, and diagonal directionality
• Each face serves not only as a physical connection but also as a data reception and transmission interface

3.2 Intent-Based Processing Model

• Requests are generated by AI and dynamically create flows based on objectives
• Network topology is not predefined, but formed in real time based on intent

3.3 AI-Driven Reconfiguration

• AI resides within the network nodes and dynamically reallocates faces based on traffic and request context
• A single face can support multi-channel operations and priority-based queuing

4. Implementation Case: Velsanet Network Structure

• Dodecahedron: Agent AI intermediary node (bridging Personal ↔ Assistant AI)
• Icosahedron: Assistant AI node managing national/global topologies
• Octahedron: Lower-tier node for user connections and parallel E2E routing

References

1. Vinton Cerf & Robert Kahn, A Protocol for Packet Network Intercommunication, IEEE Transactions on Communications, 1974.
2. J.C.R. Licklider, Man-Computer Symbiosis, IRE Transactions on Human Factors in Electronics, 1960.
3. David D. Clark, The Design Philosophy of the DARPA Internet Protocols, ACM SIGCOMM, 1988.
4. B. Leiner et al., The Past and Future History of the Internet, ACM Communications, 1997.
5. Jeff Dean & Luiz André Barroso, The Tail at Scale, Communications of the ACM, 2013.
6. Geoffrey Hinton et al., Deep Learning, Nature, 2015.
7. OpenAI, GPT-4 Technical Report, 2023.
8. Velsanet Core Development Group, Face-Based Dynamic Control Model, Internal Whitepaper, 2025.
9. Changho Song, Theoretical Analysis of Parallel E2E-Based AI Network Architecture, Velsanet Research Series, 2024.
10. NSF FIA Project, MobilityFirst and Named Data Networking, 2010–2020.

Author Declaration

This paper is not intended for submission to any academic journal or conference. As the inventor of Velsanet, I, Changho Song, assert that the ideas, models, and structures described herein are the foundation of an independent and sovereign communication paradigm. The purpose of this document is conceptual clarity, not institutional recognition.