论文标题
在室内测量中评估了FDD大规模MIMO中基于上行链路CSI的下行链路预编码的深度学习
Deep Learning for Uplink CSI-based Downlink Precoding in FDD massive MIMO Evaluated on Indoor Measurements
论文作者
论文摘要
当操作大量的多输入多输出（MIMO）系统具有上行链路（UL）和下行链路（DL）通道时，以不同的频率（频划分双工（FDD）操作）时，下行链路预编码的通道状态信息（CSI）的获取是一个重大挑战。由于除了收发器障碍之外，UL和DL CSI均取决于发射器和接收器周围的物理环境，因此有理由认为，对于静态环境，可能存在从UL CSI到DL CSI的映射。首先，我们建议使用基于神经网络（NN）的方法来学习此映射并使用经典信号处理提供基准。其次，我们介绍了一个计划，以评估所有方法的概括的性能和质量，并区分已知和以前看不见的物理位置。第三，我们评估了使用32-Antenna通道音响器收集的现实世界室内数据集上的所有方法。
When operating massive multiple-input multiple-output (MIMO) systems with uplink (UL) and downlink (DL) channels at different frequencies (frequency division duplex (FDD) operation), acquisition of channel state information (CSI) for downlink precoding is a major challenge. Since, barring transceiver impairments, both UL and DL CSI are determined by the physical environment surrounding transmitter and receiver, it stands to reason that, for a static environment, a mapping from UL CSI to DL CSI may exist. First, we propose to use various neural network (NN)-based approaches that learn this mapping and provide baselines using classical signal processing. Second, we introduce a scheme to evaluate the performance and quality of generalization of all approaches, distinguishing between known and previously unseen physical locations. Third, we evaluate all approaches on a real-world indoor dataset collected with a 32-antenna channel sounder.
