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arXiv 2202.02856eess.SPcs.AIcs.LG
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具有索引调制的深度学习辅助空间复用

Deep Learning-Aided Spatial Multiplexing with Index Modulation

Ericsson Research(爱立信研究院);Netas(内塔斯公司);Istanbul Technical University(伊斯坦布尔技术大学)

Merve Turhan, Ersin Ozturk, Hakan Ali Cirpan

摘要:

本文提出了具有索引调制(IM)的空间复用(SMX)多输入多输出(MIMO)传输的深度学习(DL)辅助数据检测(Deep-SMX-IM)。通过结合迫零(ZF)检测器和DL技术构建了Deep-SMX-IM。所提出的方法使用DL技术的显著优点来学习频域和空域的传输特性。此外,由于使用IM提供的基于子块的检测,Deep-SMX-IM是一种简单的方法,最终降低了复杂性。已经表明,与ZF检测器相比,Deep-SMX-IM具有显着的错误性能增益,而不会增加不同系统配置的计算复杂度。

英文摘要:

In this paper, deep learning (DL)-aided data detection of spatial multiplexing (SMX) multiple-input multiple-output (MIMO) transmission with index modulation (IM) (Deep-SMX-IM) has been proposed. Deep-SMX-IM has been constructed by combining a zero-forcing (ZF) detector and DL technique. The proposed method uses the significant advantages of DL techniques to learn transmission characteristics of the frequency and spatial domains. Furthermore, thanks to using subblockbased detection provided by IM, Deep-SMX-IM is a straightforward method, which eventually reveals reduced complexity. It has been shown that Deep-SMX-IM has significant error performance gains compared to ZF detector without increasing computational complexity for different system configurations.

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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