Deep-Learning-Aided Joint Channel Estimation and Data Detection for Spatial Modulation

• Published in: IEEE access Vol. 8; pp. 191910 - 191919
• Main Authors: Xiang, Luping, Liu, Yusha, Van Luong, Thien, Maunder, Robert G., Yang, Lie-Liang, Hanzo, Lajos
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Channel estimation; Channels; data detection; Data models; Deep learning; Deep neural network (DNN); Indexes; Maximum likelihood detection; Maximum likelihood estimation; Modulation; Spatial data; spatial modulation (SM)
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3032627

Deep neural network (DNN)-aided spatial modulation (SM) is conceived. In particular, a pair of DNN structures are designed for replacing the conventional model-based channel estimators and detectors. As our first prototype, the conventional DNN estimates the channel relying on the pilot symbols and then carries out data detection in a data-driven manner. By contrast, our new DeepSM scheme is proposed for operation in more realistic time-varying channels, which updates the channel state information (CSI) at each time-slot (TS) before detecting the data. Hence, our novel DeepSM scheme is capable of performing well even in highly dynamic communication environments. Finally, our simulations show that the proposed DeepSM outperforms the conventional model-based channel estimation and data detection for transmission over time-varying channels.