论文标题
用于大规模MIMO检测的模型驱动的深度学习方法
A Model-Driven Deep Learning Method for Massive MIMO Detection
论文作者
论文摘要
在本文中，通过采用深神经网络（DNN）提出了有效的大量多输入多输出（MIMO）检测器。具体而言，我们首先将现有的迭代检测算法展开到DNN结构中，以便可以通过深度学习（DL）方法实现检测任务。然后，我们在每一层引入两个辅助参数，以更好地取消多源干扰（MUI）。第一个参数是生成残差误差向量，而第二个参数是调整以前层之间的关系。我们进一步设计了训练程序，以优化具有预处理输入的辅助参数。如此派生的MIMO检测器属于模型驱动的DL类别。仿真结果表明，与现有的大型MIMO系统相比，所提出的MIMO检测器可以实现可取的检测性能。
In this paper, an efficient massive multiple-input multiple-output (MIMO) detector is proposed by employing a deep neural network (DNN). Specifically, we first unfold an existing iterative detection algorithm into the DNN structure, such that the detection task can be implemented by deep learning (DL) approach. We then introduce two auxiliary parameters at each layer to better cancel multiuser interference (MUI). The first parameter is to generate the residual error vector while the second one is to adjust the relationship among previous layers. We further design the training procedure to optimize the auxiliary parameters with pre-processed inputs. The so derived MIMO detector falls into the category of model-driven DL. The simulation results show that the proposed MIMO detector can achieve preferable detection performance compared to the existing detectors for massive MIMO systems.
