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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >CNN-Based Analog CSI Feedback in FDD MIMO-OFDM Systems
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CNN-Based Analog CSI Feedback in FDD MIMO-OFDM Systems

机译：基于CNN的模拟CSI反馈在FDD MIMO-OFDM系统中

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Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to better utilize the available spatial diversity and multiplexing gains. However, in a frequency division duplex (FDD) massive MIMO system, CSI feedback overhead degrades the overall spectral efficiency. Deep Learning (DL)-based CSI feedback compression schemes have received a lot of attention recently as they provide significant improvements in compression efficiency; however, they still require reliable feedback links to convey the compressed CSI information to the BS. Instead, we propose here a Convolutional neural network (CNN)-based analog feedback scheme, called AnalogDeepCMC, which directly maps the downlink CSI to uplink channel input. Corresponding noisy channel outputs are used by another CNN to reconstruct the downlink channel estimate. The proposed analog scheme not only outperforms existing digital CSI feedback schemes in terms of the achievable downlink rate, but also simplifies the feedback transmission as it does not require explicit quantization, coding, and modulation, and provides a low-latency alternative particularly in rapidly changing MIMO channels, where the CSI needs to be estimated and fed back periodically.
机译：大量多输入多输出（MIMO）系统需要在基站（BS）处的下行链路通道状态信息（CSI）以更好地利用可用的空间分集和多路复用增益。然而，在频分双工（FDD）大量MIMO系统中，CSI反馈开销降低了整体光谱效率。基于深度学习（DL）的CSI反馈压缩方案最近受到了很多关注，因为它们提供了压缩效率的显着改善;但是，它们仍然需要可靠的反馈链路以将压缩的CSI信息传送到BS。相反，我们在此提出了一种被称为模拟反馈方案的卷积神经网络（CNN），称为AnalogDeePCMC，其直接将下行链路CSI映射到上行链路通道输入。另一个CNN使用相应的噪声信道输出来重建下行链路信道估计。所提出的模拟方案在实现的下行链路速率方面不仅优于现有的数字CSI反馈方案，而且还简化了反馈传输，因为它不需要显式量化，编码和调制，并且特别是在快速变化中提供低延迟替代方案MIMO频道，其中CSI需要定期估计和反馈。

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