Viewing Channel as Sequence Rather Than Image: A 2-D Seq2Seq Approach for Efficient MIMO-OFDM CSI Feedback
Viewing Channel as Sequence Rather Than Image: A 2-D Seq2Seq Approach for Efficient MIMO-OFDM CSI Feedback
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DOI：
10.1109/twc.2023.3250422
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发表时间：
2023-11
影响因子：
10.4
通讯作者：
Zi-Yuan Chen;Zhaoyang Zhang;Zhu Xiao;Zhaohui Yang;Kai‐Kit Wong
中科院分区：
文献类型：
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作者：
Zi-Yuan Chen;Zhaoyang Zhang;Zhu Xiao;Zhaohui Yang;Kai‐Kit Wong
在本文中，我们的目标是设计一个有效的基于学习的信道状态信息（CSI）反馈方案的多输入多输出（MIMO）正交频分复用（OFDM）系统从物理启发的角度来看。首先，我们认为，一个MIMO-OFDM系统的CSI矩阵是物理上更接近一个二维（2-D）序列，而不是一个图像，由于其明显的不平滑性，不可扩展性，和平移的空间和频域内的变化。在此基础上，我们引入了一个2-D长短期记忆（LSTM）神经网络来表示CSI，并提出了一个2-D序列到序列（Seq 2Seq）模型用于CSI压缩和重构。具体来说，一个两层2-D LSTM用于CSI特征提取，另一个用于CSI表示和重构。与现有的基于卷积神经网络（CNN）的方案相比，该方案不仅充分利用了CSI的二维特性，而且保留了CSI矩阵的索引信息和不光滑特征。我们表明，该方案的计算复杂度是线性的发射天线和子载波的数量。它的关键性能，如重建精度，收敛速度，短期训练后的泛化能力，以及对有损反馈的鲁棒性，与现有流行的卷积网络进行了全面比较。实验结果表明，与传统的基于CNN的方法相比，在相同的开销下，我们的方案可以在重建精度上带来近7 dB的增益，在相同的精度下，可以减少高达75%的反馈开销。
In this paper, we aim to design an effective learning-based channel state information (CSI) feedback scheme for the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems from a physics-inspired perspective. We first argue that the CSI matrix of a MIMO-OFDM system is physically closer to a two-dimensional (2-D) sequence rather than an image due to its apparent unsmoothness, non-scalability, and translational variance within both the spatial and frequency domains. On this basis, we introduce a 2-D long short-term memory (LSTM) neural network to represent the CSI and propose a 2-D sequence-to-sequence (Seq2Seq) model for CSI compression and reconstruction. Specifically, one two-layer 2-D LSTM is used for CSI feature extraction, and the other is used for CSI representation and reconstruction. The proposed scheme can not only fully utilize the unique 2-D characteristics of CSI but also preserve the index information and unsmooth features of the CSI matrix compared with current convolutional neural network (CNN) based schemes. We show that the computational complexity of the proposed scheme is linear in the number of transmit antennas and subcarriers. Its key performances, like reconstruction accuracy, convergence speed, generalization ability after short-term training, and robustness to lossy feedback, are comprehensively compared with existing popular convolutional networks. Experimental results show that our scheme can bring up to nearly 7 dB gain in reconstruction accuracy under the same overhead and reduce feedback overhead by up to 75% under the same accuracy compared with the conventional CNN-based approaches.