Low Complexity Classification Approach for Faster-Than-Nyquist (FTN) Signaling Detection
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Canada
Funder
Natural Science and Engineering Research Council of Canada (NSERC) through its Discovery program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Modeling and Simulation
Link
http://xplorestaging.ieee.org/ielx7/4234/10064471/10017273.pdf?arnumber=10017273
Reference10 articles.
1. A Novel Sum-Product Detection Algorithm for Faster-Than-Nyquist Signaling: A Deep Learning Approach
2. Deep Learning-based List Sphere Decoding for Faster-than-Nyquist (FTN) Signaling Detection
3. Coded Modulation Systems
4. A Novel Low Complexity Faster-than-Nyquist (FTN) Signaling Detector for Ultra High-Order QAM
5. Reduced-Complexity Equalization for Faster-Than-Nyquist Signaling: New Methods Based on Ungerboeck Observation Model
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