Analysis of Lightweight Design and Explainability in 6G Symbiotic Intelligence and Its Driving Architectural Technologies

DOI

10.2991/978-94-6463-986-5_52How to use a DOI?

Keywords

Digital twin; Knowledge graph; Artificial intelligence

Abstract

With the global roll out and deployment of 5G networks, the sixth-generation mobile communication system (6G) is transforming towards ultra-low latency, global coverage and intelligent native. Artificial intelligence (AI), as the core driver of 6G’s intelligent transformation, has shown great potential and advantages in scenarios such as integrated perception, communication and computing (ISCC), resource optimization and network autonomy. However, the contradiction between AI’s high energy consumption, algorithmic black box characteristics and dynamic network requirements has become a key challenge restricting the sustainable development of 6G. This paper focuses on three technical directions: lightweight AI models, native interpretable architectures, and task-driven architectures, systematically analyzing their technical characteristics, application scenarios, and core challenges. By comparing the energy efficiency performance of lightweight technologies such as knowledge distillation and model pruning and combining digital twin (DT) and knowledge graph (KG) -driven verification mechanisms, a hierarchical intelligent architecture design paradigm is proposed. Experiments show that the lightweight model can reduce base station energy consumption by 62%, and the integration of interpretable tools can increase fault location accuracy to 93%[1]. This study provides some theoretical support and technical path for building efficient, reliable and green 6G networks.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yizhi Wang
PY  - 2026
DA  - 2026/02/18
TI  - Analysis of Lightweight Design and Explainability in 6G Symbiotic Intelligence and Its Driving Architectural Technologies
BT  - Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
PB  - Atlantis Press
SP  - 501
EP  - 511
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-986-5_52
DO  - 10.2991/978-94-6463-986-5_52
ID  - Wang2026
ER  -