Bayesian Neural Network Detector for an Orthogonal Time Frequency Space Modulation
Author:
Affiliation:
1. Centre of Excellence in Telecommunications, University of Sydney, Sydney, NSW, Australia
Funder
Research Training Program Stipend from The University of Sydney, Australian Research Council
ARC Laureate Fellowship
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/5962382/9976385/09900413.pdf?arnumber=9900413
Reference18 articles.
1. Low-complexity Delay-Doppler Symbol DNN for OTFS Signal Detection
2. Two-Dimensional Convolutional Neural Network-Based Signal Detection for OTFS Systems
3. A Simple Variational Bayes Detector for Orthogonal Time Frequency Space (OTFS) Modulation
4. Model-Driven Deep Learning for MIMO Detection
5. Improving Approximate Expectation Propagation Massive MIMO Detector With Deep Learning
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1. A hybrid detection algorithm for 5G OTFS waveform for 64 and 256 QAM with Rayleigh and Rician channels;Open Engineering;2024-01-01
2. Analysis of Neural Networks Based OTFS Wireless System;2023 International Conference on Emerging Techniques in Computational Intelligence (ICETCI);2023-09-21
3. Untrained Neural Network based Bayesian Detector for OTFS Modulation Systems;IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS);2023-05-20
4. Profuse Channel Estimation and Signal Detection Techniques for Orthogonal Time Frequency Space in 6G Epoch: A Survey;IEEE Access;2023
5. Reconfigurable Intelligent Surface-Aided Orthogonal Time Frequency Space and Its Deep Learning-Based Signal Detection;IEEE Access;2023
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