论文标题
Remimo：复发和置换术
RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection
论文作者
论文摘要
在本文中，我们提出了一个用于MIMO符号检测的新型神经网络。它是由无线通信系统中的几个重要考虑因素激发的。置换式均等和可变的用户数量。神经检测器学习一种迭代解码算法，该算法被用作一堆迭代单元。每个迭代单元是一个由3个子模块组成的神经计算模块：可能性模块，编码器模块和预测器模块。可能性模块将有关生成（正向）过程的信息注入神经网络。编码器predictor模块一起更新状态向量和符号估计。编码器模块更新了状态向量，并采用基于变压器的注意网络以置换模棱两可的方式处理用户之间的交互。预测器模块完善了符号估计。模块化和排列的模棱两可的体系结构允许处理不同数量的用户。由此产生的神经检测器结构是独一无二的，并且在任何先前提出的神经探测器中都表现出几种所需的特性。我们将其性能与现有方法进行比较，结果表明我们网络具有高精度有效处理变量数量的变速器的能力。
In this paper, we present a novel neural network for MIMO symbol detection. It is motivated by several important considerations in wireless communication systems; permutation equivariance and a variable number of users. The neural detector learns an iterative decoding algorithm that is implemented as a stack of iterative units. Each iterative unit is a neural computation module comprising of 3 sub-modules: the likelihood module, the encoder module, and the predictor module. The likelihood module injects information about the generative (forward) process into the neural network. The encoder-predictor modules together update the state vector and symbol estimates. The encoder module updates the state vector and employs a transformer based attention network to handle the interactions among the users in a permutation equivariant manner. The predictor module refines the symbol estimates. The modular and permutation equivariant architecture allows for dealing with a varying number of users. The resulting neural detector architecture is unique and exhibits several desirable properties unseen in any of the previously proposed neural detectors. We compare its performance against existing methods and the results show the ability of our network to efficiently handle a variable number of transmitters with high accuracy.
