# Super-resolution channel estimation for mmWave massive MIMO with hybrid precoding

## TL;DR

This paper introduces an iterative reweighting method for super-resolution channel estimation in mmWave massive MIMO systems with hybrid precoding, overcoming resolution loss of traditional compressive sensing approaches.

## Contribution

It proposes a novel IR-based super-resolution channel estimation scheme with gradient descent optimization and SVD-based preconditioning, improving accuracy over conventional methods.

## Key findings

- Enhanced channel estimation accuracy demonstrated in simulations
- Outperforms traditional compressive sensing schemes
- Reduced computational complexity through SVD preconditioning

## Abstract

Channel estimation is challenging for millimeter-wave (mmWave) massive MIMO with hybrid precoding, since the number of radio frequency (RF) chains is much smaller than that of antennas. Conventional compressive sensing based channel estimation schemes suffer from severe resolution loss due to the channel angle quantization. To improve the channel estimation accuracy, we propose an iterative reweight (IR)-based super-resolution channel estimation scheme in this paper. By optimizing an objective function through the gradient descent method, the proposed scheme can iteratively move the estimated angle of arrivals/departures (AoAs/AoDs) towards the optimal solutions, and finally realize the super-resolution channel estimation. In the optimization, a weight parameter is used to control the tradeoff between the sparsity and the data fitting error. In addition, a singular value decomposition (SVD)-based preconditioning is developed to reduce the computational complexity of the proposed scheme. Simulation results verify the better performance of the proposed scheme than conventional solutions.