# Channel Estimation for WiFi Prototype Systems with Super-Resolution Image Recovery

## TL;DR

This paper introduces a super-resolution image recovery approach for WiFi channel estimation, leveraging neural networks trained on statistical models to improve accuracy in practical MIMO systems.

## Contribution

It proposes a novel super-resolution based channel estimation method that does not rely on pre-assumed channel characteristics and demonstrates its effectiveness in real WiFi systems.

## Key findings

- Outperforms conventional methods in LOS scenarios
- Effective in NLOS environments
- Applicable to practical WiFi prototype systems

## Abstract

Channel estimation is crucial for modern WiFi system and becomes more and more challenging with the growth of user throughput in multiple input multiple output configuration. Plenty of literature spends great efforts in improving the estimation accuracy, while the interpolation schemes are overlooked. To deal with this challenge, we exploit the super-resolution image recovery scheme to model the non-linear interpolation mechanisms without pre-assumed channel characteristics in this paper. To make it more practical, we offline generate numerical channel coefficients according to the statistical channel models to train the neural networks, and directly apply them in some practical WiFi prototype systems. As shown in this paper, the proposed super-resolution based channel estimation scheme can outperform the conventional approaches in both LOS and NLOS scenarios, which we believe can significantly change the current channel estimation method in the near future.