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.

翻译：未来无线多输入多输出（MIMO）系统将整合sub-6 GHz与毫米波（mmWave）频段，以满足日益增长的高数据速率需求。MIMO链路建立通常需要精确的信道估计，而毫米波频段由于低信噪比（SNR）使该任务尤为困难。本文提出两种基于深度学习的新方法，通过利用sub-6 GHz频段的带外信息来估计毫米波MIMO信道。第一种方法采用卷积神经网络（CNN），第二种方法使用UNet架构。我们将所提方法与仅依赖带内信息的深度学习方法以及其他先进的带外辅助方法进行比较。仿真结果表明，我们提出的带外辅助深度学习方法在可达频谱效率方面优于现有方案。