A manifold learning-based CSI feedback framework for FDD massive MIMO

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.