Abstract
Existing deep learning (DL) based downlink channel prediction algorithms for frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems mainly utilize single-source sensing information, e.g., the uplink channels, to predict the downlink channels. With the aid of multi-source sensing information (MSI) in communication systems, this paper explores deep multimodal learning (DML) technologies to improve the accuracy of downlink channel prediction. By leveraging various modality combinations and fusion levels, we design several DML based architectures for downlink channel prediction, which can also be easily extended to other communication problems like beam prediction. Simulation results demonstrate that the proposed DML based architectures can effectively exploit the constructive and complementary information of multimodal sensory data, thus achieving better performance than existing works.
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