论文标题
线性MIMO预编码设计有限字母输入的设计通过无模型训练
Linear MIMO Precoders Design for Finite Alphabet Inputs via Model-Free Training
论文作者
论文摘要
本文研究了一种新的方法，该方法是针对基于自动编码器（AE）的有限字母输入来设计线性预编码的，而无需了解通道模型。通过在多输入多输出（MIMO）系统中对自动编码器进行的无模型训练，所提出的方法可以有效地解决优化问题，以设计预制器，以最大程度地在通道输入和输出之间最大化相互信息，而只有才能观察到通道的输入输出信息。具体而言，提出的方法将接收器和先编码器视为AE中的两个独立参数化函数，并分别使用精确和近似梯度进行交替训练它们。与以前的预编码器设计方法相比，它减轻了要求已知明确的通道模型的局限性。仿真结果表明，在最大化相互信息和降低位错误率的方面，该提出的方法以及在已知通道模型下的这些方法都起作用。
This paper investigates a novel method for designing linear precoders with finite alphabet inputs based on autoencoders (AE) without the knowledge of the channel model. By model-free training of the autoencoder in a multiple-input multiple-output (MIMO) system, the proposed method can effectively solve the optimization problem to design the precoders that maximize the mutual information between the channel inputs and outputs, when only the input-output information of the channel can be observed. Specifically, the proposed method regards the receiver and the precoder as two independent parameterized functions in the AE and alternately trains them using the exact and approximated gradient, respectively. Compared with previous precoders design methods, it alleviates the limitation of requiring the explicit channel model to be known. Simulation results show that the proposed method works as well as those methods under known channel models in terms of maximizing the mutual information and reducing the bit error rate.
