论文标题
大量MIMO中CSI反馈的轻量级卷积神经网络
Lightweight Convolutional Neural Networks for CSI Feedback in Massive MIMO
论文作者
论文摘要
在大规模多输入多输出系统的频划分双面模式中，必须通过反馈链接将下行链路通道状态信息（CSI）发送到基站（BS）。但是，由于反馈链接的带宽限制，将CSI传输到BS是昂贵的。深度学习（DL）最近在CSI反馈中取得了巨大的成功。实现高性能和低复杂性CSI反馈是基于DL的通信的挑战。我们在本研究中开发了基于DL的CSI反馈网络，以有效地完成CSI的反馈。但是，由于参数数量过多，该网络无法有效地应用于移动终端。因此，我们进一步提出了一个基于开发网络的新轻巧的CSI反馈网络。仿真结果表明，所提出的CSI网络比其他与CSINET相关的作品表现出更好的重建性能。此外，轻型网络保持了一些参数和参数复杂性，同时确保令人满意的重建性能。这些发现表明所提出的技术的可行性和潜力。
In frequency division duplex mode of massive multiple-input multiple-output systems, the downlink channel state information (CSI) must be sent to the base station (BS) through a feedback link. However, transmitting CSI to the BS is costly due to the bandwidth limitation of the feedback link. Deep learning (DL) has recently achieved remarkable success in CSI feedback. Realizing high-performance and low-complexity CSI feedback is a challenge in DL based communication. We develop a DL based CSI feedback network in this study to complete the feedback of CSI effectively. However, this network cannot be effectively applied to the mobile terminal because of the excessive numbers of parameters. Therefore, we further propose a new lightweight CSI feedback network based on the developed network. Simulation results show that the proposed CSI network exhibits better reconstruction performance than that of other CsiNet-related works. Moreover, the lightweight network maintains a few parameters and parameter complexity while ensuring satisfactory reconstruction performance. These findings suggest the feasibility and potential of the proposed techniques.
