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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) isproposed to reduce the feedback and improve the spectral efficiency of FDDmassive MIMO. Manifold learning (ML) is an effective method for dimensionalityreduction. However, most ML algorithms focus only on data compression, and lackthe corresponding recovery methods. Moreover, the computational complexity ishigh when dealing with incremental data. To solve these problems, we propose alandmark selection algorithm to characterize the topological skeleton of themanifold where the CSI sample resides. Based on the learned skeleton, the localpatch of the incremental CSI on the manifold can be easily determined by itsnearest landmarks. This motivates us to propose a low-complexity compressionand reconstruction scheme by keeping the local geometric relationships withlandmarks unchanged. We theoretically prove the convergence of the proposedalgorithm. Meanwhile, the upper bound on the error of approximating the CSIsamples using landmarks is derived. Simulation results under an industrialchannel model of 3GPP demonstrate that the proposed MLCF method outperformsexisting algorithms based on compressed sensing and deep learning.

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