In this article, we present a new iterative MIMO detection algorithm based on inexact alternating direction method of multipliers. Each iteration is considered as a neural network layer with learnable parameters, which are optimized by the stochastic gradient descent algorithm with a training data set of the received vectors and the ground truth transmitted signals. Numerical results show that the proposed algorithm outperforms the existing learnable detection network and it achieves near-optimal performance close to the sphere decoder in the case of a large number of receive antennas.

中文翻译：

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基于 Inexact ADMM 的可学习 MIMO 检测网络

在本文中，我们提出了一种新的基于乘法器不精确交替方向法的迭代 MIMO 检测算法。每次迭代都被视为具有可学习参数的神经网络层，这些参数通过随机梯度下降算法使用接收向量和地面实况传输信号的训练数据集进行优化。数值结果表明，所提出的算法优于现有的可学习检测网络，并且在大量接收天线的情况下实现了接近球形解码器的接近最优性能。