论文标题
低分辨率ADC的大型MIMO系统的基于DNN的检测器
DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs
论文作者
论文摘要
低分辨率类似物到数字转换器（ADC）被认为是降低大量多输入 - 多数输出（MIMO）系统的成本和功耗的实用且有希望的解决方案。不幸的是，低分辨率ADC会大大扭曲接收的信号，从而使数据检测更具挑战性。在本文中，我们开发了一个新的深神经网络（DNN）框架，以在低分辨率大规模MIMO系统中有效和低复杂性数据检测。基于重新的最大似然检测问题，我们提出了两个基于模型驱动的DNN检测器，即OBMNET和FBMNET，分别针对一位和少数数量的大型MIMO系统。拟议的OBMNET和FBMNET探测器具有专为低分辨率MIMO接收器设计的独特而简单的结构，因此可以有效地训练和实施。数值结果还表明，OBMNET和FBMNET的表现明显优于现有检测方法。
Low-resolution analog-to-digital converters (ADCs) have been considered as a practical and promising solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately, low-resolution ADCs significantly distort the received signals, and thus make data detection much more challenging. In this paper, we develop a new deep neural network (DNN) framework for efficient and low-complexity data detection in low-resolution massive MIMO systems. Based on reformulated maximum likelihood detection problems, we propose two model-driven DNN-based detectors, namely OBMNet and FBMNet, for one-bit and few-bit massive MIMO systems, respectively. The proposed OBMNet and FBMNet detectors have unique and simple structures designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented. Numerical results also show that OBMNet and FBMNet significantly outperform existing detection methods.
