RE-MIMO：递归和置换等变神经元MIMO检测
RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection
摘要：
在本文中，我们提出了一种新的神经网络的MIMO符号检测。它的动机是在无线通信系统中的几个重要的考虑，置换等方差和可变数量的用户。神经检测器学习实现为迭代单元的堆栈的迭代解码算法。每个迭代单元是一个神经计算模块，包括3个子模块：似然模块，编码器模块和预测器模块。似然模块将关于生成（前向）过程的信息注入神经网络。编码器-预测器模块一起更新状态向量和符号估计。编码器模块更新状态向量，并采用基于Transformer的注意力网络以置换等变的方式处理用户之间的交互。预测器模块细化符号估计。模块化和置换等变架构允许处理不同数量的用户。由此产生的神经检测器架构是独特的，并表现出几个理想的属性在任何以前提出的神经检测器。我们将其性能与现有方法进行比较，结果表明我们的网络能够有效地处理可变数量的发射机，具有高精度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
