AI 中文总结
本文针对6G双边AI模型现有部署假设的局限，提出集成5G NR协议栈、构建紧凑模型表、采用无梯度零阶微调的方案，推动其实际部署并指出开放挑战。
AI 中文摘要
对于下一代空中接口，双边人工智能（AI）模型受到越来越多的关注，这类模型同时部署在发射端和接收端，用于高效的信道反馈与数据通信。然而，现有研究中常见的假设为其实际部署带来了阻碍，包括与 legacy 用户隔离、在预定义信道条件下训练，以及基于梯度的微调需要大量跨厂商通信。本文重新审视这些假设并提出实用替代方案：第一，为实现 legacy 共存，我们将双边模型处理集成到5G新空口（NR）协议栈中，并在真实测试平台上验证其与传统NR的协同运行；第二，我们不采用在大量预定义信道条件下训练的方式，而是通过联合优化双边模型与可训练代理信道构建紧凑模型表，根据当前信道条件选择最优模型，以实现高任务性能、低训练与存储开销的信道自适应；第三，不同于现有需交换包含潜在私有模型信息的大梯度向量的微调方式，我们提出仅需标量反馈的无梯度零阶微调，促进多厂商互操作性。这些方法推动了双边AI模型的实际部署，同时凸显了关键开放挑战。
英文摘要
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.
