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arXiv 2208.02466cs.ITeess.SPmath.IT
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基于无模型训练的有限字母输入线性MIMO预编码器设计

Linear MIMO Precoders Design for Finite Alphabet Inputs via Model-Free Training

Chen Cao, Biqian Feng, Yongpeng Wu, Derrick Wing Kwan Ng, Wenjun Zhang

摘要:

本文研究了一种在不知道信道模型的情况下基于自编码器(AE)设计具有有限字母输入的线性预编码器的新方法。通过对多输入多输出(MIMO)系统中自编码器进行无模型训练,该方法可以有效地解决在仅能观测到信道输入输出信息的情况下,使信道输入输出之间的互信息最大化的预编码器设计优化问题。具体而言,该方法将接收器和预编码器视为AE中的两个独立的参数化函数,并分别使用精确梯度和近似梯度交替训练它们。与以往的预编码器设计方法相比,该方法克服了需要已知信道模型的局限性。仿真结果表明,该方法在最大化互信息和降低误比特率出错率方面与已知信道模型下的方法效果相当。

英文摘要:

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.

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。

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