# Millimeter Wave MIMO Channel Estimation Based on Adaptive Compressed Sensing

## TL;DR

This paper introduces a novel adaptive compressed sensing approach for mmWave MIMO channel estimation, utilizing a continuous basis pursuit-based dictionary and low-complexity algorithms, validated through realistic simulations.

## Contribution

It proposes a new CBP-based beamforming dictionary and two efficient algorithms for sparse channel estimation in mmWave MIMO systems, improving accuracy and reducing computational complexity.

## Key findings

- CBP-based dictionary outperforms grid-based methods in accuracy
- Proposed algorithms achieve better performance with less computation
- Simulation results confirm improved spectral efficiency

## Abstract

Multiple-input multiple-output (MIMO) systems are well suited for millimeter-wave (mmWave) wireless communications where large antenna arrays can be integrated in small form factors due to tiny wavelengths, thereby providing high array gains while supporting spatial multiplexing, beamforming, or antenna diversity. It has been shown that mmWave channels exhibit sparsity due to the limited number of dominant propagation paths, thus compressed sensing techniques can be leveraged to conduct channel estimation at mmWave frequencies. This paper presents a novel approach of constructing beamforming dictionary matrices for sparse channel estimation using the continuous basis pursuit (CBP) concept, and proposes two novel low-complexity algorithms to exploit channel sparsity for adaptively estimating multipath channel parameters in mmWave channels. We verify the performance of the proposed CBP-based beamforming dictionary and the two algorithms using a simulator built upon a three-dimensional mmWave statistical spatial channel model, NYUSIM, that is based on real-world propagation measurements. Simulation results show that the CBP-based dictionary offers substantially higher estimation accuracy and greater spectral efficiency than the grid-based counterpart introduced by previous researchers, and the algorithms proposed here render better performance but require less computational effort compared with existing algorithms.