摘要：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.
© 2019 IEEE.

ISSN号：1550-3607

卷、期、页：v 2019-May,

发表日期：2019-05-01

期刊分区（SCI为中科院分区）：无

收录情况：EI（工程索引）

发表期刊名称：IEEE International Conference on Communications

通讯作者：程星,闫松,史文哲,赵阳

第一作者：刘得军

论文类型：会议论文

论文概要：程星,刘得军,闫松,史文哲,赵阳，Channel Estimation and Equalization Based on Deep BLSTM for FBMC-OQAM Systems，IEEE International Conference on Communications，2019，v 2019-May,

论文题目：Channel Estimation and Equalization Based on Deep BLSTM for FBMC-OQAM Systems