Paper Info
Title
Ofdm Receiver Using Deep Learning: Redundancy Issues
Abstract
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.
Year
DOI
Venue
2020
10.23919/Eusipco47968.2020.9287725
28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020)
Keywords
DocType
ISSN
deep-learning, channel-estimation, symbol-detection, OFDM, minimum-redundancy
Conference
2076-1465
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Marcele O. K. Mendonca132.06
Paulo S. R. Diniz224738.72