Artificial intelligence (AI) based downlink channel state information (CSI)
prediction for frequency division duplexing (FDD) massive multiple-input
multiple-output (MIMO) systems has attracted growing attention recently.
However, existing works focus on the downlink CSI prediction for the users
under a given environment and is hard to adapt to users in new environment
especially when labeled data is limited. To address this issue, we formulate
the downlink channel prediction as a deep transfer learning (DTL) problem,
where each learning task aims to predict the downlink CSI from the uplink CSI
for one single environment. Specifically, we develop the direct-transfer
algorithm based on the fully-connected neural network architecture, where the
network is trained on the data from all previous environments in the manner of
classical deep learning and is then fine-tuned for new environments. To further
improve the transfer efficiency, we propose the meta-learning algorithm that
trains the network by alternating inner-task and across-task updates and then
adapts to a new environment with a small number of labeled data. Simulation
results show that the direct-transfer algorithm achieves better performance
than the deep learning algorithm, which implies that the transfer learning
benefits the downlink channel prediction in new environments. Moreover, the
meta-learning algorithm significantly outperforms the direct-transfer algorithm
in terms of both prediction accuracy and stability, which validates its
effectiveness and superiority.

本文提出一种结合深度迁移学习和元学习的算法，以预测频分双工大规模多输入多输出系统下行信道状态信息 (CSI)。模拟结果表明，该算法在新环境中具有更好的表现，且元学习算法比直接迁移算法具有更高的预测准确性和稳定性。