In massive multiple-input multiple-output (MIMO) systems, the channel state information (CSI) feedback enables performance gain in frequency division duplex networks. However, with the increase in the number of antennas, the feedback overhead of CSI will also enhance. To this end, this study addresses the issue of massive MIMO CSI reconstruction using convolutional neural network (CNN), long short-term memory (LSTM) and attention mechanism, and proposes an efficient network architecture (denoted as CNN-LSTM-A). To achieve a compromise between performance and complexity, the proposed method significantly reduces the number of training parameters by utilising a single-stage network rather than a multiple-stage network. Finally, simulation results show that the authors method can reduce the feedback overhead of CSI effectively, and achieves better performance in terms of CSI compression and recovery accuracy compared with existing state-of-the-art methods.

中文翻译：

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使用CNN-LSTM和注意力机制的大规模MIMO CSI重建

在大规模多输入多输出（MIMO）系统中，信道状态信息（CSI）反馈可实现频分双工网络中的性能提升。但是，随着天线数量的增加，CSI的反馈开销也会增加。为此，本研究解决了使用卷积神经网络（CNN），长短期记忆（LSTM）和注意力机制进行大规模MIMO CSI重建的问题，并提出了一种有效的网络架构（称为CNN-LSTM-A）。为了在性能和复杂性之间取得折衷，所提出的方法通过利用单级网络而不是多级网络来显着减少训练参数的数量。最后，仿真结果表明，作者方法可以有效降低CSI的反馈开销，