深度多模态学习：融合感官数据进行大规模多输入多输出信道预测
Deep Multimodal Learning: Merging Sensory Data for Massive MIMO Channel Prediction
摘要：
现有的工作在智能通信中，最近作出了初步的尝试，利用多源传感信息（MSI），以提高系统的性能。然而，MSI辅助智能通信的研究尚未探索如何整合和融合多模态传感数据，这促使我们开发一个系统的框架，无线通信基于深度多模态学习（DML）。本文首先对基于DML的无线通信框架进行了完整的描述和启发性的理解，并从通信的角度分析了核心设计的选择。然后，我们开发了几个基于DML的架构，在大规模的多输入多输出（MIMO）系统，利用各种形式的组合和融合水平的信道预测。大规模多输入多输出信道预测的案例研究提供了一个重要的例子，可以遵循在开发其他基于DML的通信技术。仿真结果表明，所提出的DML框架可以有效地利用各种无线通信场景中多模态感知数据的建设性和互补性信息。
英文摘要：
Existing work in intelligent communications has recently made preliminary attempts to utilize multi-source sensing information (MSI) to improve the system performance. However, the research on MSI aided intelligent communications has not yet explored how to integrate and fuse the multimodal sensory data, which motivates us to develop a systematic framework for wireless communications based on deep multimodal learning (DML). In this paper, we first present complete descriptions and heuristic understandings on the framework of DML based wireless communications, where core design choices are analyzed in the view of communications. Then, we develop several DML based architectures for channel prediction in massive multiple-input multiple-output (MIMO) systems that leverage various modality combinations and fusion levels. The case study of massive MIMO channel prediction offers an important example that can be followed in developing other DML based communication technologies. Simulations results demonstrate that the proposed DML framework can effectively exploit the constructive and complementary information of multimodal sensory data in various wireless communication scenarios.
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