A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback
A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback
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DOI：
10.1109/twc.2021.3103120
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发表时间：
2022-02-01
影响因子：
10.4
通讯作者：
Ding, Zhi
中科院分区：
文献类型：
--
作者：
Liu, Zhenyu;del Rosario, Mason;Ding, Zhi
信道状态信息（CSI）在大规模MIMO通信系统的调度和容量逼近传输优化中起着至关重要的作用。在频分双工（FDD）MIMO系统中，发射端的前向链路CSI重构依赖于接收端的CSI反馈，必须在重构精度和反馈带宽之间进行权衡。递归神经网络（RNN）的最新应用已经证明了大规模MIMO CSI反馈压缩的有希望的结果。然而，与RNN深度学习相关的计算和内存成本仍然很高。在这项工作中，我们利用通道的时间相干性，以提高学习精度和反馈效率。利用马尔可夫模型，我们开发了一个基于深度卷积神经网络（CNN）的框架，称为MarkovNet，可以有效地编码CSI反馈，以提高准确性和效率。我们探索了重要的物理见解，包括输入数据的球形归一化和反馈压缩中的深度学习网络优化。我们证明，MarkovNet提供了一个实质性的性能改进和计算复杂性降低基于RNN的工作。我们展示了MarkovNet的性能在不同的MIMO配置和反馈间隔和速率的范围。使用MarkovNet的CSI恢复优于基于RNN的CSI估计，而计算成本只有一小部分。
Channel state information (CSI) plays a vital role in scheduling and capacity-approaching transmission optimization of massive MIMO communication systems. In frequency division duplex (FDD) MIMO systems, forward link CSI reconstruction at transmitter relies on CSI feedback from receiving nodes and must carefully weigh the tradeoff between reconstruction accuracy and feedback bandwidth. Recent application of recurrent neural networks (RNN) has demonstrated promising results of massive MIMO CSI feedback compression. However, the cost of computation and memory associated with RNN deep learning remains high. In this work, we exploit channel temporal coherence to improve learning accuracy and feedback efficiency. Leveraging a Markovian model, we develop a deep convolutional neural network (CNN)-based framework called MarkovNet to efficiently encode CSI feedback to improve accuracy and efficiency. We explore important physical insights including spherical normalization of input data and deep learning network optimizations in feedback compression. We demonstrate that MarkovNet provides a substantial performance improvement and computational complexity reduction over the RNN-based work. We demonstrate MarkovNet's performance under different MIMO configurations and for a range of feedback intervals and rates. CSI recovery with MarkovNet outperforms RNN-based CSI estimation with only a fraction of computational cost.