Rethinking the Foundations of Two-Sided AI Models for 6G
Yongjeong Oh Zihan Chen Timothy J. O'Shea Junyong Shin Jinho Choi Yo-Seb Jeon Jihong Park
作者信息
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.引用本文复制引用
Yongjeong Oh,Zihan Chen,Timothy J. O'Shea,Junyong Shin,Jinho Choi,Yo-Seb Jeon,Jihong Park.Rethinking the Foundations of Two-Sided AI Models for 6G[EB/OL].(2026-08-24)[2026-09-01].https://arxiv.org/abs/2608.22918.学科分类
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