论文标题
大规模MIMO CSI反馈的马尔可夫模型驱动的深度学习框架
A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback
论文作者
论文摘要
向前通道状态信息（CSI）通常在调度和容量的传输优化方面对大规模多输入多输出（MIMO）通信系统起着至关重要的作用。在频划分双面（FDD）大规模MIMO系统中，发射机的前链接CSI重建依赖于接收节点的CSI反馈，并且必须仔细权衡重建精度和反馈带宽之间的权衡。关于复发性神经网络（RNN）使用的最新研究表明，尽管计算和记忆的成本仍然很高，但对于大规模的MIMO部署而言，尽管计算和记忆的成本仍然很高。在这项工作中，我们会及时利用通道连贯性，以大大提高反馈效率。使用马尔可夫模型，我们开发了一个基于深卷积神经网络（CNN）的框架Markovnet，以及时地编码向前的CSI，以有效提高重建精度。此外，我们探讨了重要的物理见解，包括输入数据的球形归一化和用于反馈压缩的卷积层。我们证明了我们拟议的Markovnet准确恢复了远期CSI估计值的基于RNN的工作的性能改善和复杂性降低。我们探讨了反馈量化的其他实际考虑因素，并表明Markovnet以计算成本的一小部分优于基于RNN的CSI估计网络。
Forward channel state information (CSI) often plays a vital role in scheduling and capacity-approaching transmission optimization for massive multiple-input multiple-output (MIMO) communication systems. In frequency division duplex (FDD) massive MIMO systems, forwardlink CSI reconstruction at the transmitter relies critically on CSI feedback from receiving nodes and must carefully weigh the tradeoff between reconstruction accuracy and feedback bandwidth. Recent studies on the use of recurrent neural networks (RNNs) have demonstrated strong promises, though the cost of computation and memory remains high, for massive MIMO deployment. In this work, we exploit channel coherence in time to substantially improve the feedback efficiency. Using a Markovian model, we develop a deep convolutional neural network (CNN)-based framework MarkovNet to differentially encode forward CSI in time to effectively improve reconstruction accuracy. Furthermore, we explore important physical insights, including spherical normalization of input data and convolutional layers for feedback compression. We demonstrate substantial performance improvement and complexity reduction over the RNN-based work by our proposed MarkovNet to recover forward CSI estimates accurately. We explore additional practical consideration in feedback quantization, and show that MarkovNet outperforms RNN-based CSI estimation networks at a fraction of the computational cost.
