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首页> 外文期刊>IEEE Transactions on Broadcasting >A Supplementary Explanation for Experimental Environment of “Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters”
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A Supplementary Explanation for Experimental Environment of “Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters”

机译:“传统MIMO发射机的深度学习信号检测实施方法”实验环境的补充说明

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摘要

To solve practical challenges for implementing deep learning-based algorithm in MIMO signal detector such as handling complex number or designing proper neural network for a specific communication system, (Baek et al., 2019) has proposed candidate implementation methodologies with simple verification experiments. According to (Baek et al., 2019), it was shown that the proposed algorithms can achieve the optimal ML performance. However, due to the lack of explanation on the experimental environment, it is difficult for readers to reproduce the presented experiments and obtain the same results. This document precisely explains on the experimental environments of (Baek et al., 2019) including the exact channel profiles.
机译:为了解决在MIMO信号检测器中实现基于深度学习的算法的实际挑战,例如处理复数或为特定通信系统设计适当的神经网络,(BAEK等人,2019)已经提出了具有简单验证实验的候选实施方法。据(Baek等人,2019),结果表明,所提出的算法可以实现最佳的ML性能。但是,由于对实验环境缺乏解释,读者难以再现所提出的实验并获得相同的结果。本文档精确解释了(BAEK等,2019)的实验环境,包括确切的频道概况。

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