# FCFGS-CV-Based Channel Estimation for Wideband MmWave Massive MIMO Systems with Low-Resolution ADCs

## TL;DR

This paper introduces a novel FCFGS-CV-based channel estimation method for wideband mmWave massive MIMO systems with low-resolution ADCs, leveraging sparsity and cross-validation to improve accuracy.

## Contribution

It proposes a new channel estimator combining FCFGS algorithm with cross-validation for better performance in low-resolution mmWave MIMO systems.

## Key findings

- Improved channel estimation accuracy in simulations.
- Effective detection of overfitting using CV.
- Enhanced robustness with low-resolution ADCs.

## Abstract

In this paper, the fully corrective forward greedy selection-cross validation-based (FCFGS-CV-based) channel estimator is proposed for wideband millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs). The sparse nature of the mmWave virtual channel in the angular and delay domains is exploited to convert the maximum a posteriori (MAP) channel estimation problem to an optimization problem with a concave objective function and sparsity constraint. The FCFGS algorithm, which is the generalized orthogonal matching pursuit (OMP) algorithm, is used to solve the sparsity-constrained optimization problem. Furthermore, the CV technique is adopted to determine the proper termination condition by detecting overfitting when the sparsity level is unknown.