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：近期，一种基于高斯混合模型（GMM）的通用有限反馈方案被提出，用于频分双工（FDD）系统。该方案在不同系统参数下具有高度灵活性，适用于点对点多输入多输出（MIMO）及多用户MIMO（MU-MIMO）通信场景。所学习的GMM能够覆盖基站小区内所有移动终端（MT）的运行模式，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。本研究通过合并或剪枝冗余混合分量，利用单一学习GMM将基于GMM的反馈方案扩展至可变反馈长度。此外，基于模型认知约束GMM协方差为Toeplitz或循环结构。这些扩展显著降低了数据卸载量，提升了GMM聚类能力，进而改善系统性能。点对点与多用户系统的仿真结果验证了所提扩展方案的有效性。