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

Nurettin Turan, Benedikt Fesl, Michael Joham, Zhengxiang Ma, Anthony C. K. Soong, Baoling Sheen, Weimin Xiao, Wolfgang Utschick
Computer Science, Information Theory, Information Theory (cs.IT), Signal Processing (eess.SP)
2024-01-03 00:00:00
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.
PDF: Limited Feedback on Measurements: Sharing a Codebook or a Generative Model?.pdf
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