The autoencoder empowered by artificial intelligence has shown considerable potential in solving channel state information (CSI) feedback problems in frequency-division duplexing systems. However, this method needs to completely change the existing feedback schemes, which is difficult to deploy in the next few years. This paper proposes an environment knowledge-aided codebook-based CSI feedback framework, which retains the existent codebook-based scheme while introducing environment knowledge to feedback process through neural networks (NNs) at the base station. Only an NN-based refining operation is added after the common standardized feedback approach. The NNs learn to automatically extract environment features and utilize the channel statistics through large volumes of recorded data. The NNs also use the partial correlation between bidirectional channels to further improve feedback performance. In addition, to deal with downlink channel estimation errors, we propose two strategies to reduce their effects using an NN-based denoise module. The proposed framework can be easily embedded in most existing codebook-based feedback methods, such as random vector quantization. Two channel datasets generated by QuaDRiGa and measured in practical systems are adopted to evaluate the proposed methods. Results show that the proposed method offers over 100% increase in the throughput compared with the baseline codebook because of more accurate feedback.

中文翻译：

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使用人工智能环境知识辅助大规模 MIMO 反馈码本增强


人工智能支持的自动编码器在解决频分双工系统中的信道状态信息（CSI）反馈问题方面显示出巨大的潜力。但这种方法需要彻底改变现有的反馈方案，在未来几年内很难部署。本文提出了一种基于环境知识辅助码本的CSI反馈框架，该框架保留了现有的基于码本的方案，同时通过基站的神经网络（NN）将环境知识引入反馈过程。仅在通用标准化反馈方法之后添加基于神经网络的精炼操作。神经网络学习自动提取环境特征并通过大量记录数据利用通道统计数据。神经网络还利用双向通道之间的部分相关性来进一步提高反馈性能。此外，为了处理下行链路信道估计误差，我们提出了两种策略来使用基于神经网络的降噪模块来减少其影响。所提出的框架可以轻松嵌入到大多数现有的基于码本的反馈方法中，例如随机矢量量化。采用由 QuaDRiGa 生成并在实际系统中测量的两个通道数据集来评估所提出的方法。结果表明，由于反馈更准确，与基线码本相比，所提出的方法的吞吐量提高了 100% 以上。