RC-Struct: Reservoir Computing Meets Knowledge of Structure in MIMO-OFDM
Jiarui Xu, Zhou Zhou, Lianjun Li, Lizhong Zheng, Lingjia Liu · 2021 IEEE Globecom Workshops (GC Wkshps) · 2021
This paper introduces a structure-based neural network architecture, namely RC-Struct, for MIMO-OFDM symbol detection. The RC-Struct exploits the temporal structure of the MIMO-OFDM signals through reservoir computing (RC). A binary classifier is built to perform the multi-class detection by leveraging the repetitive constellation structure in the communication system. The incorporation of RC allows the RC-Struct to be learned in a purely online fashion with extremely limited pilot symbols in each OFDM subframe. The binary classifier efficiently utilizes the precious online training symbols and allows an easy extension to high-order modulations without a substantial increase in complexity. The experiment demonstrates the effectiveness of RC-Struct in the MIMO-OFDM system with the dynamically adapted link. The results shed light on combining communication domain knowledge and learning-based receive processing for 5G and 5G Beyond.