Quantized RIS-Aided mmWave Massive MIMO Channel Estimation With Uniform Planar Arrays

TL;DR: The material covered includes tensor rank and rank decomposition; basic tensor factorization models and their relationships and properties; broad coverage of algorithms ranging from alternating optimization to stochastic gradient; statistical performance analysis; and applications ranging from source separation to collaborative filtering, mixture and topic modeling, classification, and multilinear subspace learning.

TL;DR: The preliminary outcomes of extensive research on mmWave massive MIMO are presented and emerging trends together with their respective benefits, challenges, and proposed solutions are highlighted to point out current trends, evolving research issues and future directions on this technology.

TL;DR: Simulation results show that the proposed method can provide an accurate channel estimate and achieve a substantial training overhead reduction and the inherent sparsity in mmWave channels is exploited.

TL;DR: This paper considers a “vector AMP” (VAMP) algorithm and shows that VAMP has a rigorous scalar state-evolution that holds under a much broader class of large random matrices A: those that are right-orthogonally invariant.