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Discrete Fourier transform (DFT) codebook-based solutions arewell-established for limited feedback schemes in frequency division duplex(FDD) systems. In recent years, data-aided solutions have been shown to achievehigher performance, enabled by the adaptivity of the feedback scheme to thepropagation environment of the base station (BS) cell. In particular, aversatile limited feedback scheme utilizing Gaussian mixture models (GMMs) wasrecently introduced. The scheme supports multi-user communications, exhibitslow complexity, supports parallelization, and offers significant flexibilityconcerning various system parameters. Conceptually, a GMM captures environmentknowledge and is subsequently transferred to the mobile terminals (MTs) foronline inference of feedback information. Afterward, the BS designs precodersusing either directional information or a generative modeling-based approach. Amajor shortcoming of recent works is that the assessed system performance isonly evaluated through synthetic simulation data that is generally unable tofully characterize the features of real-world environments. It raises thequestion of how the GMM-based feedback scheme performs on real-worldmeasurement data, especially compared to the well-established DFT-basedsolution. Our experiments reveal that the GMM-based feedback schemetremendously improves the system performance measured in terms of sum-rate,allowing to deploy systems with fewer pilots or feedback bits.

Limited Feedback on Measurements: Sharing a Codebook or a Generative Model?