Millimeter wave (mmWave) multiple-input-multi-output (MIMO) is now a reality with great potential for further improvement. We study full-duplex transmissions as an effective way to improve mmWave MIMO systems. Compared to half-duplex systems, full-duplex transmissions may offer higher data rates and lower latency. However, full-duplex transmission is hindered by self-interference (SI) at the receive antennas, and SI channel estimation becomes a crucial step to make the full-duplex systems feasible. In this paper, we address the problem of channel estimation in full-duplex mmWave MIMO systems using neural networks (NNs). Our approach involves sharing pilot resources between user equipments (UEs) and transmit antennas at the base station (BS), aiming to reduce the pilot overhead in full-duplex systems and to achieve a comparable level to that of a half-duplex system. Additionally, in the case of separate antenna configurations in a full-duplex BS, providing channel estimates of transmit antenna (TX) arrays to the downlink UEs poses another challenge, as the TX arrays are not capable of receiving pilot signals. To address this, we employ an NN to map the channel from the downlink UEs to the receive antenna (RX) arrays to the channel from the TX arrays to the downlink UEs. We further elaborate on how NNs perform the estimation with different architectures, (e.g., different numbers of hidden layers), the introduction of non-linear distortion (e.g., with a 1-bit analog-to-digital converter (ADC)), and different channel conditions (e.g., low-correlated and high-correlated channels). Our work provides novel insights into NN-based channel estimators.

翻译：毫米波多输入多输出技术已成为现实，并具有巨大的改进潜力。我们研究将全双工传输作为提升毫米波MIMO系统性能的有效途径。与半双工系统相比，全双工传输可提供更高的数据速率和更低的时延。然而，接收天线端的自干扰会阻碍全双工传输，而自干扰信道估计是实现全双工系统的关键步骤。本文采用神经网络解决全双工毫米波MIMO系统中的信道估计问题。我们的方法通过在基站端共享用户设备与发射天线之间的导频资源，旨在降低全双工系统的导频开销，使其达到与半双工系统相当的水平。此外，在全双工基站采用分离式天线配置的情况下，由于发射天线阵列无法接收导频信号，如何为下行用户设备提供发射天线阵列的信道估计构成了另一项挑战。为此，我们采用神经网络将下行用户设备到接收天线阵列的信道映射至发射天线阵列到下行用户设备的信道。我们进一步阐述了神经网络如何通过不同架构（例如不同数量的隐藏层）、引入非线性失真（例如采用1位模数转换器）以及在不同信道条件（例如低相关与高相关信道）下执行估计任务。本研究为基于神经网络的信道估计器提供了新的见解。