论文标题
通过利用频道时间相关性，对部分MIMO CSI反馈进行深度学习
Deep Learning for Partial MIMO CSI Feedback by Exploiting Channel Temporal Correlation
论文作者
论文摘要
需要对DL CSI进行准确的估计，以实现大型MIMO系统中的高光谱和能源效率。先前的工作已经在FDD系统中开发了基于学习的CSI反馈框架，以有效地编码和恢复，并有效效益。但是，通过接收终端进行CSI估计的下行链路飞行员可能会占据大量的资源元素，以实现大量天线和折衷频谱效率。为了克服这个问题，我们提出了一种新的基于学习的反馈体系结构，以通过利用CSI时间相关性来有效地编码交错的非重叠天线子阵列的部分CSI反馈。为了易于编码，我们进一步设计了一种ifft方法，以将天线子阵列的部分CSI切除并保持部分CSI稀疏性。我们的结果表明，在室内/室外场景中，提议的模型在室内/室外方案中表现出色，以大大降低计算功率和存储需求。
Accurate estimation of DL CSI is required to achieve high spectrum and energy efficiency in massive MIMO systems. Previous works have developed learning-based CSI feedback framework within FDD systems for efficient CSI encoding and recovery with demonstrated benefits. However, downlink pilots for CSI estimation by receiving terminals may occupy excessively large number of resource elements for massive number of antennas and compromise spectrum efficiency. To overcome this problem, we propose a new learning-based feedback architecture for efficient encoding of partial CSI feedback of interleaved non-overlapped antenna subarrays by exploiting CSI temporal correlation. For ease of encoding, we further design an IFFT approach to decouple partial CSI of antenna subarrays and to preserve partial CSI sparsity. Our results show superior performance in indoor/outdoor scenarios by the proposed model for CSI recovery at significantly reduced computation power and storage needs.
