The fundamental designs of wireless systems toward AI-Native 6G and beyond are driven by the need for ever-increasing demand of mobile data traffic, extreme spectral efficiency, and adaptability across diverse service scenarios. To overcome the limitations posed by feedback-based multiple-input and multiple-output (MIMO) transmission, we propose a novel frequency-domain Correlation-aware Feedback-free MIMO Transmission and Resource Allocation (CaFTRA) framework tailored for fully-decoupled radio access networks (FD-RAN) to meet the emerging requirements of AI-Native 6G and beyond. By leveraging artificial intelligence (AI), CaFTRA effectively eliminates real-time uplink feedback by predicting channel state information (CSI) based solely on user geolocation. We introduce a Learnable Queries-driven Transformer Network for CSI mapping from user geolocation, which utilizes multi-head attention and learnable query embeddings to accurately capture frequency-domain correlations among resource blocks (RBs), thereby significantly improving the precision of CSI prediction. Once base stations (BSs) adopt feedback-free transmission, their downlink transmission coverage can be significantly expanded due to the elimination of frequent uplink feedback. To enable efficient resource scheduling under such extensive-coverage scenarios, we apply a low-complexity many-to-one matching theory-based algorithm for efficient multi-BS association and multi-RB resource allocation, which is proven to converge to a stable matching within limited iterations. Simulation results demonstrate that CaFTRA achieves stable matching convergence and significant gains in spectral efficiency and user fairness compared to 5G, underscoring its potential value for 6G standardization efforts.

翻译：无线系统向AI原生6G及未来网络演进的基础设计，受移动数据流量持续增长的需求、极致频谱效率以及跨多样性服务场景适应性的驱动。为克服基于反馈的多输入多输出（MIMO）传输的局限性，我们提出一种新型频域相关感知无反馈MIMO传输与资源分配（CaFTRA）框架，该框架专为全解耦无线接入网络（FD-RAN）设计，以满足AI原生6G及未来网络的新兴需求。通过利用人工智能（AI），CaFTRA仅基于用户地理位置预测信道状态信息（CSI），从而有效消除实时上行反馈。我们引入一种可学习查询驱动的Transformer网络，用于从用户地理位置映射CSI，该网络利用多头注意力机制和可学习查询嵌入，精确捕捉资源块（RB）间的频域相关性，从而显著提升CSI预测精度。当基站（BS）采用无反馈传输时，由于消除了频繁的上行反馈，其下行传输覆盖范围可大幅扩展。为在此类广覆盖场景下实现高效资源调度，我们应用一种基于低复杂度多对一匹配理论的算法，用于高效的多BS关联与多RB资源分配，该算法被证明能在有限迭代内收敛至稳定匹配。仿真结果表明，与5G相比，CaFTRA实现了稳定匹配收敛，并在频谱效率和用户公平性方面取得显著增益，凸显其对6G标准化工作的潜在价值。