  In this paper, we introduce 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 leverages the repetitive constellation structure in the
system to perform multi-class detection. 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 enables the efficient
utilization of the precious online training symbols and allows an easy
extension to high-order modulations without a substantial increase in
complexity. Experiments show that the introduced RC-Struct outperforms both the
conventional model-based symbol detection approaches and the state-of-the-art
learning-based strategies in terms of bit error rate (BER). The advantages of
RC-Struct over existing methods become more significant when rank and link
adaptation are adopted. The introduced RC-Struct sheds light on combining
communication domain knowledge and learning-based receive processing for
5G/5G-Advanced and Beyond.
