Adaptive Neural Network-based OFDM Receivers
Author:
Affiliation:
1. University of Stuttgart,Institute of Telecommunications,Stuttgart,Germany,70569
2. NVIDIA,Berlin,Germany,10623
3. Toyota Motor North America,InfoTech Lab
Funder
Ministry of Education
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9833808/9833812/09833970.pdf?arnumber=9833970
Reference9 articles.
1. End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication
2. Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems
3. Toward a 6G AI-Native Air Interface
4. Wiener Filter versus Recurrent Neural Network-based 2D-Channel Estimation for V2X Communications
5. Adam: A method for stochastic optimization;kingma,2014
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2. Envisioning Physical Layer Flexibility Through the Power of Machine-Learning;2023 IEEE Globecom Workshops (GC Wkshps);2023-12-04
3. A Signal processing method of OFDM communication receiver based on CNN;Physical Communication;2023-08
4. Learning Joint Detection, Equalization and Decoding for Short-Packet Communications;IEEE Transactions on Communications;2023-02
5. A Near-Optimal Deep Detector for BER Minimization in an OFDM AF Relay System;2022 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS);2022-12-28
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