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 and 5G Beyond.

本文介绍了一种基于结构的神经网络架构，名为RC-Struct，用于MIMO-OFDM符号检测。RC-Struct通过储备计算(RC)利用MIMO-OFDM信号的时域结构。一个二进制分类器利用系统中重复的星座结构进行多类检测。RC的引入使RC-Struct可以在每个OFDM子帧中仅使用非常有限的导频符号纯在线学习。二进制分类器实现了宝贵的在线培训符号的高效利用，并允许轻松扩展到高阶调制，而不会大幅增加复杂度。实验表明，引入的RC-Struct在误比特率（BER）方面优于传统的基于模型的符号检测方法和现有的基于学习的策略。当采用等级和链路自适应时，RC-Struct的优点比现有方法更为显着。引入的RC-Struct为5G / 5G-Advanced及其他领域的通信领域知识和学习-based接收处理的组合提供了启示。

RC-Struct: 一种基于结构的神经网络方法用于MIMO-OFDM检测