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.

翻译：在本文中,我们引入了基于结构的神经网络结构,即RC-Struct,用于MIMO-OFDM符号检测。RC-Struct通过储油层计算(RC)探索MIMO-OFDM信号的时间结构。二进制分类器利用系统中重复的星座结构进行多级检测。纳入RC-Struct,使RC-Struct能够以纯在线方式学习,每个DM子框架的试点标志都极为有限。二进制分类器能够有效利用宝贵的在线培训符号,便于在不大幅增加复杂度的情况下将高排序调制扩展至高排序。实验显示,引入的RC-Struct超越了基于常规模型的符号检测方法和基于现代学习的比特错误率战略(BER)。在采用等级和链接适应时,RC-Struct相对于现有方法的优势就更加显著。引入了RC-Struct 将通信域知识与学习基础的M/G/5G-VA-Verd 接收5G-A-Side-Ad-Serent处理。