论文标题
了解结构知识：一种基于神经网络的MIMO-OFDM检测方法
Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection
论文作者
论文摘要
在本文中，我们探讨了基于神经网络的策略，用于在MIMO-OFDM系统中执行符号检测。在基于储层计算（RC）的符号检测方法的基础上，我们引入了对称且分解的二进制决策神经网络，以利用Mimo-Ofdm系统中固有的结构知识。具体来说，利用星座知识的频域中添加了二进制决策神经网络。我们表明，引入的对称神经网络可以将原始的$ m $ - ARY检测问题分解为一系列二进制分类任务，从而大大降低了神经网络检测器的复杂性，同时通过有限的培训开销提供了良好的概括性能。数值评估表明，引入的混合RC-Binary决策检测框架在低SNR状态中使用不完美的通道状态信息（CSI）的符号错误率（CSI）在基于最大似然模型的符号检测方法上执行。
In this paper, we explore neural network-based strategies for performing symbol detection in a MIMO-OFDM system. Building on a reservoir computing (RC)-based approach towards symbol detection, we introduce a symmetric and decomposed binary decision neural network to take advantage of the structure knowledge inherent in the MIMO-OFDM system. To be specific, the binary decision neural network is added in the frequency domain utilizing the knowledge of the constellation. We show that the introduced symmetric neural network can decompose the original $M$-ary detection problem into a series of binary classification tasks, thus significantly reducing the neural network detector complexity while offering good generalization performance with limited training overhead. Numerical evaluations demonstrate that the introduced hybrid RC-binary decision detection framework performs close to maximum likelihood model-based symbol detection methods in terms of symbol error rate in the low SNR regime with imperfect channel state information (CSI).
