论文标题
基于深度学习的CSI反馈，用于在单细胞和多细胞大型MIMO系统中进行波束形成
Deep Learning-Based CSI Feedback for Beamforming in Single- and Multi-cell Massive MIMO Systems
论文作者
论文摘要
大量多输入多输出（MIMO）的电势均基于基站（BS）的可用瞬时通道状态信息（CSI）。因此，频划分双工（FDD）系统中的用户必须继续向BS馈回CSI，从而占据了大型上行链路传输资源。最近，深度学习（DL）在CSI反馈中取得了巨大的成功。但是，现有作品只是专注于提高反馈准确性，而忽略对以下模块的影响，例如波束形成（BF）。在本文中，我们提出了一个基于DL的CSI反馈框架，用于BF设计，称为CSIFBNET。 CSIFBNET的关键思想是最大化BF性能增益，而不是反馈精度。我们将其应用于两个代表性的方案：单细胞系统和多细胞系统。单细胞系统中的CSIFBNET-S基于自动编码器体系结构，在该体系结构中，用户处的编码器压缩了BS的CSI和解码器生成BF矢量。 Multicell系统中的CSIFBNET-M必须馈回两种CSI：所需的和干扰的CSI。整个神经网络都通过无监督的学习策略进行培训。模拟结果表明，与常规的基于DL的CSI反馈方法相比，CSIFBNET的性能提高和复杂性降低。
The potentials of massive multiple-input multiple-output (MIMO) are all based on the available instantaneous channel state information (CSI) at the base station (BS). Therefore, the user in frequency-division duplexing (FDD) systems has to keep on feeding back the CSI to the BS, thereby occupying large uplink transmission resources. Recently, deep learning (DL) has achieved great success in the CSI feedback. However, the existing works just focus on improving the feedback accuracy and ignore the effects on the following modules, e.g., beamforming (BF). In this paper, we propose a DL-based CSI feedback framework for BF design, called CsiFBnet. The key idea of the CsiFBnet is to maximize the BF performance gain rather than the feedback accuracy. We apply it to two representative scenarios: single- and multi-cell systems. The CsiFBnet-s in the single-cell system is based on the autoencoder architecture, where the encoder at the user compresses the CSI and the decoder at the BS generates the BF vector. The CsiFBnet-m in the multicell system has to feed back two kinds of CSI: the desired and the interfering CSI. The entire neural networks are trained by an unsupervised learning strategy. Simulation results show the great performance improvement and complexity reduction of the CsiFBnet compared with the conventional DL-based CSI feedback methods.
