Deep Learning-based mmWave MIMO Channel Estimation using sub-6 GHz Channel Information: CNN and UNet Approaches

TL;DR

This paper introduces deep learning methods using CNN and UNet architectures to improve mmWave MIMO channel estimation by leveraging sub-6 GHz information, significantly enhancing spectral efficiency.

Contribution

It presents two novel out-of-band aided deep learning approaches for mmWave MIMO channel estimation, outperforming existing methods.

Findings

Out-of-band aided methods outperform in-band only approaches.

CNN and UNet architectures improve spectral efficiency.

Proposed methods outperform state-of-the-art alternatives.

Abstract

Future wireless multiple-input multiple-output (MIMO) systems will integrate both sub-6 GHz and millimeter wave (mmWave) frequency bands to meet the growing demands for high data rates. MIMO link establishment typically requires accurate channel estimation, which is particularly challenging at mmWave frequencies due to the low signal-to-noise ratio (SNR). In this paper, we propose two novel deep learning-based methods for estimating mmWave MIMO channels by leveraging out-of-band information from the sub-6 GHz band. The first method employs a convolutional neural network (CNN), while the second method utilizes a UNet architecture. We compare these proposed methods against deep-learning methods that rely solely on in-band information and with other state-of-the-art out-of-band aided methods. Simulation results show that our proposed out-of-band aided deep-learning methods outperform existing alternatives in terms of achievable spectral efficiency.