论文标题
Ancinet：一种有效的深度学习方法，用于大规模MIMO系统中估计CSI的反馈压缩
AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems
论文作者
论文摘要
准确的通道状态信息（CSI）反馈在改善大规模多输入多输出（M-MIMO）系统的性能增长方面起着至关重要的作用，在这种情况下，困境与有限的反馈bandwith相比，困境过多的CSI高架头顶。通过考虑由于不完善的通道估计而引起的嘈杂的CSI，我们提出了一种新型的深神经网络结构，即Ancinet，以有限的带宽进行CSI反馈。 Ancinet从嘈杂的CSI样品中提取无噪声特征，以实现有效的CSI压缩以进行反馈。实验结果验证了所提出的ANCINET方法在各种条件下都优于现有技术。
Accurate channel state information (CSI) feedback plays a vital role in improving the performance gain of massive multiple-input multiple-output (m-MIMO) systems, where the dilemma is excessive CSI overhead versus limited feedback bandwith. By considering the noisy CSI due to imperfect channel estimation, we propose a novel deep neural network architecture, namely AnciNet, to conduct the CSI feedback with limited bandwidth. AnciNet extracts noise-free features from the noisy CSI samples to achieve effective CSI compression for the feedback. Experimental results verify that the proposed AnciNet approach outperforms the existing techniques under various conditions.
