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.

中文翻译：

---

RC-Struct：用于 MIMO-OFDM 检测的基于结构的神经网络方法

在本文中，我们介绍了一种基于结构的神经网络架构，即 RC-Struct，用于 MIMO-OFDM 符号检测。RC-Struct 通过存储计算 (RC) 来利用 MIMO-OFDM 信号的时间结构。二元分类器利用系统中的重复星座结构来执行多类检测。RC 的结合允许以纯在线方式学习 RC-Struct，每个 OFDM 子帧中的导频符号极为有限。二元分类器能够有效利用宝贵的在线训练符号，并允许在不显着增加复杂性的情况下轻松扩展到高阶调制。实验表明，在误码率 (BER) 方面，引入的 RC-Struct 优于传统的基于模型的符号检测方法和最先进的基于学习的策略。当采用秩和链路自适应时，RC-Struct 相对于现有方法的优势变得更加显着。引入的 RC-Struct 阐明了将通信领域知识与基于学习的接收处理相结合，以实现 5G/5G-Advanced 及更高版本。