论文标题
Polardensenet：MIMO系统中CSI反馈的深度学习模型
PolarDenseNet: A Deep Learning Model for CSI Feedback in MIMO Systems
论文作者
论文摘要
在多输入多输出（MIMO）系统中，需要在基站（BS）的高分辨率通道信息（CSI），以确保最佳性能，尤其是在多用户MIMO（MU-MIMO）系统的情况下。如果没有频道互惠的频道双工（FDD）系统，则用户需要将CSI发送到BS。通常，与FDD系统中的CSI反馈相关的大型开销通常会成为改善系统性能的瓶颈。在本文中，我们提出了一种基于AI的CSI反馈，基于自动编码器体系结构，该反馈将UE的CSI编码为低维的潜在空间，并通过有效地减少在恢复过程中损失的同时有效地减少反馈的空间来将其解码为BS。我们的仿真结果表明，基于AI的提议架构的表现优于最先进的高分辨率线性组合代码簿，使用5G新广播（NR）系统中采用的DFT基础。
In multiple-input multiple-output (MIMO) systems, the high-resolution channel information (CSI) is required at the base station (BS) to ensure optimal performance, especially in the case of multi-user MIMO (MU-MIMO) systems. In the absence of channel reciprocity in frequency division duplex (FDD) systems, the user needs to send the CSI to the BS. Often the large overhead associated with this CSI feedback in FDD systems becomes the bottleneck in improving the system performance. In this paper, we propose an AI-based CSI feedback based on an auto-encoder architecture that encodes the CSI at UE into a low-dimensional latent space and decodes it back at the BS by effectively reducing the feedback overhead while minimizing the loss during recovery. Our simulation results show that the AI-based proposed architecture outperforms the state-of-the-art high-resolution linear combination codebook using the DFT basis adopted in the 5G New Radio (NR) system.
