Recently, a versatile limited feedback scheme based on a Gaussian mixture model (GMM) was proposed for frequency division duplex (FDD) systems. This scheme provides high flexibility regarding various system parameters and is applicable to both point-to-point multiple-input multiple-output (MIMO) and multi-user MIMO (MU-MIMO) communications. The GMM is learned to cover the operation of all mobile terminals (MTs) located inside the base station (BS) cell, and each MT only needs to evaluate its strongest mixture component as feedback, eliminating the need for channel estimation at the MT. In this work, we extend the GMM-based feedback scheme to variable feedback lengths by leveraging a single learned GMM through merging or pruning of dispensable mixture components. Additionally, the GMM covariances are restricted to Toeplitz or circulant structure through model-based insights. These extensions significantly reduce the offloading amount and enhance the clustering ability of the GMM which, in turn, leads to an improved system performance. Simulation results for both point-to-point and multi-user systems demonstrate the effectiveness of the proposed extensions.

翻译：最近，针对频分双工（FDD）系统提出了一种基于高斯混合模型（GMM）的灵活有限反馈方案。该方案对各种系统参数具有高度灵活性，适用于点对点多输入多输出（MIMO）和多用户MIMO（MU-MIMO）通信。通过对GMM进行训练，使其覆盖基站（BS）小区内所有移动终端（MT）的运作情况，每个MT仅需评估其最强混合分量作为反馈，无需在MT端进行信道估计。在本工作中，我们通过合并或剪枝冗余混合分量，利用单一训练好的GMM将基于GMM的反馈方案扩展至可变反馈长度。此外，基于模型先验知识，将GMM协方差矩阵限制为托普利兹或循环结构。这些扩展显著降低了卸载数据量并增强了GMM的聚类能力，从而提升了系统性能。针对点对点和多用户系统的仿真结果验证了所提扩展方案的有效性。