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A Note on Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters

机译:关于传统MIMO发射器的深度学习信号检测实施方法的说明

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摘要

Baek et al. proposed a deep learning-based signal detector for conventional MIMO systems, which is a pioneering work of applying artificial intelligence into wireless communications. Although this work works well under static fading channels, it is worth notable that it fails to work under block-fading channels. In particular, the detection BER under block-fading channels is around 0.5 even in the high regime of SNR. To explain this unexpected result, we provide some simple yet efficient theoretical analysis, which clearly verifies that the proposed detector cannot decouple the phases between the channel parameters and transmitted signals and hence it fails to detect the transmitted signals under block-fading channels. The results in this paper can help understand and improve the detector. In particular, the detector structure should incorporate the channel state information for the application under block-fading channels.
机译:BAEK等人。提出了一种用于传统MIMO系统的基于深度学习的信号检测器,其是将人工智能应用于无线通信的开创性工作。虽然这项工作在静态衰落渠道下运作良好,但值得注意的是,它无法在逐渐消退渠道下工作。特别是,即使在SNR的高端,横向衰落通道下的检测BER约为0.5。为了解释这一意外结果,我们提供了一些简单但高效的理论分析,这清楚地验证了所提出的探测器不能在信道参数和发送信号之间解耦的阶段,因此它不能检测块衰落信道下的发送信号。本文的结果可以帮助理解和改善探测器。特别地,检测器结构应该在块衰落信道下包含用于应用的信道状态信息。

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