摘要：Filter bank multicarrier (FBMC) modulation is a promising candidate modulation method for future communication systems. However, FBMC systems cannot directly use channel estimation methods proposed for orthogonal frequency-division multiplexing systems due to its inherent imaginary interference. In this letter, we propose a channel estimation and equalization scheme based on deep learning (DL-CE) for FBMC systems. In the DL-CE scheme, the channel state information and the constellation demapping method are learned by a deep neural networks model, and then the distorted frequencydomain sequences are equalized implicitly to obtain binary bits directly. Simulation results show that the proposed DL-CE scheme achieves state-of-the-art performance on channel estimation and equalization.

关键字：Filter bank multicarrier OQAM channel estimation deep learning deep neural networks

ISSN号：2162-2337

卷、期、页：卷: 8 期: 3 页: 881-884

发表日期：2019-06-01

影响因子：0.000000

收录情况：SCIE（科学引文索引网络版）,ESI（基本科学指标数据库）,EI（工程索引）

发表期刊名称：IEEE Wireless Communications Letters

通讯作者：程星,王辰,闫松,朱正宇

第一作者：刘得军

论文类型：期刊论文

论文概要：程星,刘得军,王辰,闫松,朱正宇，Deep Learning-Based Channel Estimation and Equalization Scheme for FBMC/OQAM Systems，IEEE Wireless Communications Letters，2019，卷: 8 期: 3 页: 881-884

论文题目：Deep Learning-Based Channel Estimation and Equalization Scheme for FBMC/OQAM Systems