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A manifold learning-based CSI feedback framework for FDD massive MIMO by Cao, Yandi; Yin, Haifan; Qin, Ziao; Li, Weidong; Wu, Weimin; Debbah, Mérouane is a scholarly article available to read on EtoBox.

What is A manifold learning-based CSI feedback framework for FDD massive MIMO about?

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 for 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. Considering to utilize the intrinsic manifold structure where the CSI samples reside, we propose a landmark selection algorithm to describe the topological skeleton of this manifold. 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 an incremental CSI compression and reconstruction scheme by keeping the local geometric relationships with landmarks invariant. We theoretically prove the convergence of the proposed landmark selection algorithm. Meanwhile, the upper bound on the error of approxi

Author

Cao, Yandi; Yin, Haifan; Qin, Ziao; Li, Weidong; Wu, Weimin; Debbah, Mérouane

Published

2023

Language

EN