OFDM Receiver Using Deep Learning: Redundancy Issues

被引:0
作者
Mendonca, Marcele O. K. [1 ,2 ]
Diniz, Paulo S. R. [1 ,2 ]
机构
[1] Univ Fed Rio de Janeiro, SMT Signals Multimedia & Telecommun Lab, DEL Poli, POB 68504, BR-21941972 Rio De Janeiro, RJ, Brazil
[2] Univ Fed Rio de Janeiro, PEE, COPPE, POB 68504, BR-21941972 Rio de Janeiro, RJ, Brazil
来源
28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020) | 2021年
关键词
deep-learning; channel-estimation; symbol-detection; OFDM; minimum-redundancy;
D O I
暂无
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
To combat the inter-symbol interference (ISI) and the inter-block interference (IBI) caused by multi-path fading in orthogonal frequency-division multiplexing (OFDM) systems, it is usually recommended employing a cyclic prefix (CP) with length equal to the channel order. In some practical cases, however, the channel order is not exactly known. Looking for a balance between a full-sized CP and its absence, we investigate the redundancy issues and propose a minimum redundancy OFDM receiver using deep-learning (DL) tools. In this way, we can benefit from an improved reception performance, when compared with CP-free case, and also a better spectrum utilization when compared with the CP-OFDM case. Moreover, compared with the CP-free case, improved performance can be obtained even when the channel order is not available. Simulation results indicate that a good BER level can be achieved and the proposed technique can also be applied in other DL-based receivers.
引用
收藏
页码:1687 / 1691
页数:5
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