Massive multi-input multi-output (MIMO) in Frequency Division Duplex (FDD) mode suffers from heavy feedback overhead for Channel State Information (CSI). In this paper, a novel manifold learning-based CSI feedback framework (MLCF) is proposed to reduce the feedback and improve the spectral efficiency of FDD massive MIMO. Manifold learning (ML) is an effective method for dimensionality reduction. However, most ML algorithms focus only on data compression, and lack the corresponding recovery methods. Moreover, the computational complexity is high when dealing with incremental data. To solve these problems, we propose a landmark selection algorithm to characterize the topological skeleton of the manifold where the CSI sample resides. Based on the learned skeleton, the local patch of the incremental CSI on the manifold can be easily determined by its nearest landmarks. This motivates us to propose a low-complexity compression and reconstruction scheme by keeping the local geometric relationships with landmarks unchanged. We theoretically prove the convergence of the proposed algorithm. Meanwhile, the upper bound on the error of approximating the CSI samples using landmarks is derived. Simulation results under an industrial channel model of 3GPP demonstrate that the proposed MLCF method outperforms existing algorithms based on compressed sensing and deep learning.

翻译：在频分双工（FDD）模式下的大规模多输入多输出（MIMO）系统中，信道状态信息（CSI）反馈开销巨大。本文提出一种基于流形学习的新型CSI反馈框架（MLCF），旨在降低反馈开销并提升FDD大规模MIMO系统的频谱效率。流形学习（ML）是一种有效的降维方法，但现有大多数ML算法仅关注数据压缩而缺乏相应的重建方法，且在处理增量数据时计算复杂度较高。为解决这些问题，我们提出一种地标选择算法，用于刻画CSI样本所在流形的拓扑骨架。基于学习得到的骨架，增量CSI在流形上的局部邻域可通过其最近地标轻松确定。这启发我们提出一种低复杂度的压缩与重建方案：保持与地标之间的局部几何关系不变。理论上证明了所提算法的收敛性，同时推导了利用地标近似CSI样本的误差上界。在3GPP工业信道模型下的仿真结果表明，所提MLCF方法优于基于压缩感知和深度学习的现有算法。