CGAN-Based CSI Fusion for Frequency Division Duplex (FDD) Massive MIMO Systems

Massive Multiple Input Multiple Output (MIMO) is a cornerstone technology for achieving high capacity and spectral efficiency. A key challenge in frequency division duplex (FDD) massive MIMO systems lies in obtaining accurate downlink (DL) channel state information (CSI), as the absence of uplink (UL)DL reciprocity hinders conventional estimation methods, creating a bottleneck in system performance. To address this issue, various approaches have been developed, broadly categorized into Conversion and Feedback approaches. However, partial reciprocity and quantization loss in feedback codebook design make accurate DL CSI acquisition a persistent challenge. In this paper, we propose a novel CGAN (Conditional Generative Adversarial Network)-based CSI-fusion framework that integrates both the UL channel statistics obtained from sounding reference signals (SRS) and the DL feedback from user equipments (UEs). These two sources of DL CSI-related information are fused and utilized as conditional inputs to CGAN to map the UL channel statistics to DL CSI. The proposed CGAN-based CSI-Fusion framework significantly enhances DL CSI acquisition accuracy, offering a practical solution to overcoming DL CSI acquisition challenges.