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.

翻译：离散傅里叶变换（DFT）码本方案已在频分双工（FDD）系统的有限比特反馈中确立为成熟技术。近年来，数据辅助方案通过使反馈机制自适应适应基站（BS）小区的传播环境，展现出更高的性能。具体而言，近期提出了一种基于高斯混合模型（GMM）的通用有限比特反馈方案。该方案支持多用户通信、具备低复杂度、支持并行化，并在多种系统参数上提供显著灵活性。从概念上看，GMM捕获环境知识，随后将其传输至移动终端（MT）用于在线推断反馈信息，进而使基站基于方向信息或生成式建模方法设计预编码器。近期研究的主要不足在于：系统性能评估仅依赖于合成仿真数据，而此类数据通常无法完全表征真实环境特征。这引发了关键问题：基于GMM的反馈方案在实际测量数据中的表现如何？特别是相较于成熟的DFT方案，其性能差异如何？实验表明，相比DFT方案，基于GMM的反馈方案在系统总速率性能上实现了显著提升，可支持部署导频或反馈开销更低的系统。