LAUSR

For implementation of massive multiple-input multiple-output (MIMO) cellular systems in frequency division duplex (FDD) mode, accurate estimation of downlink channel state information (CSI) is necessary, but full radio channel reciprocity between the uplink and downlink does not exist in that mode. Existing work on estimating downlink CSI in FDD massive MIMO systems has considered such approaches as angle-of-arrival reciprocity, compressive sensing, using second-order channel statistics (particularly the channel covariance matrix (CCM)), and machine learning using deep neural networks (DNNs). Typical DNN-based approaches are unsuitable for this problem because DNNs require large datasets, thousands of training epochs, and are susceptible to environmental variations. To overcome these shortcomings, we develop a conditional generative adversarial network (CGAN) approach to uplink-to-downlink mapping of both CCMs and CSI. To apply this method, we convert the uplink and downlink CCMs/CSI to images and employ CGAN techniques previously applied to image translation. The normalized mean square error performance of the proposed CGAN is evaluated for several array sizes for both CCM and CSI mapping. For uplink-to-downlink CSI mapping, we also examine the spectral efficiency performance of our CGAN-based method, as well as the impact of pilot reuse; both simulated and measured CSI data are considered. Our results demonstrate performance improvement over existing algorithms.