  Discrete Fourier transform (DFT) codebook-based solutions are
well-established for limited feedback schemes in frequency division duplex
(FDD) systems. In recent years, data-aided solutions have been shown to achieve
higher performance, enabled by the adaptivity of the feedback scheme to the
propagation environment of the base station (BS) cell. In particular, a
versatile limited feedback scheme utilizing Gaussian mixture models (GMMs) was
recently introduced. The scheme supports multi-user communications, exhibits
low complexity, supports parallelization, and offers significant flexibility
concerning various system parameters. Conceptually, a GMM captures environment
knowledge and is subsequently transferred to the mobile terminals (MTs) for
online inference of feedback information. Afterward, the BS designs precoders
using either directional information or a generative modeling-based approach. A
major shortcoming of recent works is that the assessed system performance is
only evaluated through synthetic simulation data that is generally unable to
fully characterize the features of real-world environments. It raises the
question of how the GMM-based feedback scheme performs on real-world
measurement data, especially compared to the well-established DFT-based
solution. Our experiments reveal that the GMM-based feedback scheme
tremendously improves the system performance measured in terms of sum-rate,
allowing to deploy systems with fewer pilots or feedback bits.
