Rethinking the Foundations of Two-Sided AI Models for 6G

2026-08-24

AI Summary

本文针对6G中双侧AI模型的部署问题，通过将其集成到5G NR协议栈、优化模型以适应当前信道条件及采用零阶微调方法来减少训练和存储开销，提出了实用解决方案。

Abstract

For next-generation air interfaces, two-sided artificial intelligence (AI) models have received growing attention, with AI models deployed at both the transmitter and receiver for efficient channel feedback and data communication. However, their practical deployment is complicated by assumptions commonly made in existing studies, including isolation from legacy users, training under predefined channel conditions, and gradient-based fine-tuning requiring substantial cross-vendor communication. This article revisits these assumptions and presents practical alternatives. First, for legacy coexistence, we integrate two-sided model processing into the 5G New Radio (NR) protocol stack and validate its operation alongside conventional NR on a real-world testbed. Second, instead of training under a massive number of predefined channel conditions, we construct a compact model table by jointly optimizing two-sided models with trainable surrogate channels, and select the best model according to the current channel condition to enable channel adaptation with high task performance and low training/storage overhead. Finally, unlike existing fine-tuning that exchanges large gradient vectors containing potentially private model information, we present gradient-free zeroth-order fine-tuning that requires only scalar feedback, facilitating multi-vendor interoperability. Together, these approaches advance the practical deployment of two-sided AI models while highlighting key open challenges.

Problem

• two-sided AI models
• practical deployment
• legacy coexistence
• channel conditions
• fine-tuning

Innovation

• two-sided AI models
• 5G New Radio (NR) protocol stack
• trainable surrogate channels
• gradient-free zeroth-order fine-tuning
• multi-vendor interoperability