Reservoir Computing Meets Extreme Learning Machine in Real-Time MIMO-OFDM Receive Processing
Reservoir Computing Meets Extreme Learning Machine in Real-Time MIMO-OFDM Receive Processing
复制标题
储层计算在实时 MIMO-OFDM 接收处理中遇到极限学习机
DOI：
10.1109/tcomm.2022.3141399
复制
发表时间：
2022
影响因子：
8.3
通讯作者：
Yi, Yang
中科院分区：
文献类型：
--
作者：
Li, Lianjun;Liu, Lingjia;Zhou, Zhou;Yi, Yang
在本文中，我们考虑了一种基于实时深度学习的MIMO-OFDM系统符号检测方法。为了利用无线信道的时间相关性和OFDM信号的时频结构，提出了一种具有深度前馈输出层的递归神经网络（RNN），其中递归层和前馈输出层分别处理时域和频域信息。选择一种特殊类型的RNN——储层计算（RC）和一种特殊类型的前馈神经网络——极限学习机（ELM）作为相应的构建块，以方便空中训练。引入在线训练损失目标，实时递归更新神经权值。我们认为这是文献中首次实现MIMO-OFDM符号检测的实时机器学习，即在OFDM符号基础上进行基于nn的符号检测。我们证明了(1)ieee标准化WiFi训练序列可以直接用作实时训练序列(2)使用我们理论推导的导频模式可以进一步提高符号检测性能。评估结果表明，我们基于rc - elm的符号检测方法在高动态信道环境中优于传统的基于模型的技术以及最先进的基于学习的方法，用于实时符号检测。
In this paper, we consider a real-time deep learning-based symbol detection approach for MIMO-OFDM systems. To exploit the temporal correlation of the wireless channel and the time-frequency structure of OFDM signals, a recurrent neural network (RNN) with deep feedforward output layers is introduced, where the recurrent layers and feedforward output layers are designed to process time-domain and frequency-domain information respectively. Reservoir computing (RC), a special type of RNN, and extreme learning machine (ELM), a special type of feedforward neural network, are chosen as the corresponding building blocks to facilitate over-the-air training. An online training loss objective is introduced to recursively update the neural weights in real-time. We believe this is the first work in the literature to realize real-time machine learning for MIMO-OFDM symbol detection, i.e., conducting NN-based symbol detection on an OFDM symbol basis. We demonstrate that (1) theIEEEstandardized WiFi training sequence can be directly applied as the real-time training sequence (2) the symbol detection performance can be further improved by using our theoretically derived pilot pattern. Evaluation results show that our RC-ELM-based symbol detection method outperforms traditional model-based techniques as well as state-of-the-art learning-based approaches in highly dynamic channel environments for real-time symbol detection.