This paper proposes a multi-input multi-output (MIMO) channel prediction scheme using multi-task learning algorithm. Based on the spatially correlated MIMO channel Channel State Information (CSI) observations, a multi-task least square support vector machine (MTLS-SVM) is trained, where the CSI prediction for each antenna pair can be modeled as one task and jointly learning between these tasks are implemented. Then the future CSI is predicted by this MTLS-SVM. By using the relatedness of the multiple tasks, the spatial correlations between different antenna pairs can fully be exploited and hence better channel prediction performance can be achieved compared with the single task prediction scheme.

本文提出了一种基于多任务学习算法的多输入多输出（MIMO）信道预测方案。基于空间相关的MIMO信道信道状态信息（CSI）观测值，训练了多任务最小二乘支持向量机（MTLS-SVM），其中每个天线对的CSI预测可以建模为一项任务，并在这些任务已实现。然后，通过此MTLS-SVM预测将来的CSI。通过使用多个任务的相关性，可以充分利用不同天线对之间的空间相关性，因此与单个任务预测方案相比，可以获得更好的信道预测性能。