Multi-Weight Nuclear Norm Minimization for Low-Rank Matrix Recovery in Presence of Subspace Prior Information - Ardakani, Hamideh Sadat Fazael; Daei, Sajad; Haddadi, Farzan (IEEE transactions on signal processing, 2022)
Weighted nuclear norm minimization has been recently recognized as a technique for reconstruction of a low-rank matrix from compressively sampled measurements when some prior information about the column and row subspaces of the matrix is available. We derive the conditions and the associated recovery guarantees of weighted nuclear norm minimization when multiple weights are allowed. This setup could be used when one has access to prior subspaces forming multiple angles with the column and row subspaces of the ground-truth matrix. While existing works in this field use a single weight to penalize all the angles, we propose a multi-weight problem which is designed to penalize each angle independently using a distinct weight. Specifically, we prove that our proposed multi-weight problem is robust under weaker conditions for the measurement operator than the analogous conditions for single-weight scenario and standard nuclear norm minimization. Moreover, it provides better reconstruction error than the state- of-the-art methods. We illustrate our results with extensive numerical experiments that demonstrate the advantages of allowing multiple weights in the recovery procedure. Our work has beneficial implications for channel estimation in multiple-input multiple output (MIMO) wireless communications based on Frequency Division Duplexing (FDD). The existing methods for channel estimation in this application require a huge number of pilot (training) signals to estimate the downlink channel which greatly wastes the spectrum resources in massive MIMO systems. We provide a dynamic channel estimation scenario for FDD massive MIMO systems and show how our method could be applied to enhance the spectral efficiency.