CaFTRA: Frequency-Domain Correlation-Aware Feedback-Free MIMO Transmission and Resource Allocation for 6G and Beyond

TL;DR

The paper tackles the CSI feedback bottleneck in MIMO for next-generation networks by introducing CaFTRA, a feedback-free, geolocation-driven MIMO framework tailored for FD-RAN. It leverages a Learnable Queries-driven Transformer Network to map user geolocation to full RB CSI, enabling frequency-domain correlation-aware predictions without uplink CSI feedback. MAC-layer resource allocation is handled via a low-complexity many-to-one matching (M3-MAMA) that enables multi-BS association and multi-RB allocation with proven convergence. Numerical results in Vienna 5G simulations show CaFTRA achieves competitive throughput, superior mobility resilience, and significant gains in spectral efficiency and fairness compared to 5G baselines, highlighting its potential for 6G standardization.

Abstract

The fundamental design of wireless systems toward AI-native 6G and beyond is 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.

Figures (14)

• Figure 1: The Proposed CaFTRA Framework in FD-RAN.
• Figure 2: Working Flow of Frequency-Domain Correlation-Aware Feedback-Free MIMO Transmission Mechanism.
• Figure 3: Schematic Diagram of the LQTN-based CSI Prediction Model.
• Figure 4: Schematic Diagram of the Multi-Head Attention Mechanism.
• Figure 5: 2D (left) and 3D (right) Views of the Peng Cheng Laboratory Scenario in Vienna 5G System-Level Simulator.