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Channel Estimation and Equalization Based on Deep BLSTM for FBMC-OQAM Systems

机译：FBMC-OQAM系统基于深度BLSTM的信道估计和均衡

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Channel estimation and equalization is one of the challenges of the filter bank multicarrier (FBMC) systems because of the existence of intrinsic imaginary interference. In this paper, we try to solve this challenge from a learning-based perspective. Based on the study of the intrinsic relationship between bidirectional long short-term memory (BLSTM) recurrent neural networks (RNN) and the FBMC radio signals, we propose a novel deep BLSTM network based channel estimation and equalization scheme, abbreviated as BLSTM-CE scheme. In the BLSTM-CE scheme, the input and output of some traditional independent modules are seen as an unknown nonlinear mapping and use a deep BLSTM network to approximate it. Numerical simulation shows that our proposed BLSTM-CE scheme can obtain perfect channel estimation performance in the FBMC systems.
机译：由于存在固有虚部干扰，信道估计和均衡是滤波器组多载波（FBMC）系统的挑战之一。在本文中，我们尝试从基于学习的角度解决这一挑战。在研究双向长短期记忆（BLSTM）递归神经网络（RNN）与FBMC无线电信号之间的固有关系的基础上，我们提出了一种基于深度BLSTM网络的新型信道估计和均衡方案，简称BLSTM-CE方案。在BLSTM-CE方案中，一些传统独立模块的输入和输出被视为未知的非线性映射，并使用深层BLSTM网络对其进行近似。数值仿真表明，我们提出的BLSTM-CE方案可以在FBMC系统中获得理想的信道估计性能。

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