论文标题
基于卷积自我注意的多用户mimo demapper
Convolutional Self-Attention-Based Multi-User MIMO Demapper
论文作者
论文摘要
在基于正交频施加多路复用（OFDM）的无线通信系统中，位错误率（BER）性能在很大程度上取决于通道估计的准确性。对于一个良好的频道估计器而言，重要的是能够处理由于用户的移动性而发生的无线通道条件中的变化。近年来，重点一直放在开发基于复杂的神经网络（NN）的频道估计器上，该估计值可以使误差性能接近与精灵辅助通道估计器的误差性能。这项工作考虑了另一种选择，该替代方法是具有一个简单的通道估计器，但基于NN的更复杂的Demapper，用于为每个传输位生成软信息。特别是，解决了不完善的通道估计器的不良影响的问题，并提出了一个基于卷积的自我注意力的神经demapper，提出了基线的表现明显优于基线。
In orthogonal frequency division multiplexing (OFDM)-based wireless communication systems, the bit error rate (BER) performance is heavily dependent on the accuracy of channel estimation. It is important for a good channel estimator to be capable of handling the changes in the wireless channel conditions that occur due to the mobility of the users. In recent years, the focus has been on developing complex neural network (NN)- based channel estimators that enable an error performance close to that of a genie-aided channel estimator. This work considers the other alternative which is to have a simple channel estimator but a more complex NN-based demapper for the generation of soft information for each transmitted bit. In particular, the problem of reversing the adverse effects of an imperfect channel estimator is addressed, and a convolutional self-attention-based neural demapper that significantly outperforms the baseline is proposed.
