Turbo Channel Covariance Conversion in Massive MIMO Frequency Division Duplex Systems

Estimating the downlink (DL) channel covariance matrix (CCM) is crucial for beamforming and capacity optimization in massive MIMO frequency division duplexing (FDD) systems, yet it poses significant challenges due to the lack of direct channel reciprocity. To address this issue, a turbo channel covariance conversion (Turbo-CCC) algorithm is proposed to enhance estimation accuracy and robustness by utilizing the angular power spectrum (APS) reciprocity. Specifically, based on the electromagnetic wave propagation characteristics, we model the APS as multikernel functions. On this basis, we then develop the Turbo-CCC algorithm by integrating the orthogonal approximate message passing (OAMP) algorithm and the multikernel adaptive filtering (MKAF) algorithm based on a Bayesian framework. The OAMP module estimates the APS from the uplink (UL) CCM regardless of its structural characteristics, whereas the MKAF module refines the APS estimation by leveraging its structural characteristics. These two modules operate iteratively, progressively improving the accuracy of the DL CCM estimation. Simulation results demonstrate that the proposed algorithm noticeably enhances the estimation performance and exhibits strong adaptability to diverse APS distributions and propagation environments, offering a novel approach for the DL CCM estimation in massive MIMO FDD systems.