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Journal Article Auto-Encoder Based Orthogonal Time Frequency Space Modulation and Detection With Meta-Learning
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Authors
Jaehyun Park, Jun-Pyo Hong, Hyungju Kim, Byung Jang Jeong
Issue Date
2023-05
Citation
IEEE Access, v.11, pp.43008-43018
ISSN
2169-3536
Publisher
Institute of Electrical and Electronics Engineers Inc.
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1109/ACCESS.2023.3271993
Abstract
To tackle a Doppler sensitivity problem of orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS) has been investigated, where information is carried over delay-Doppler domain. In this paper, to improve communication reliability in doubly dispersive channel, an auto-encoder (AE)-based OTFS modulation and detection scheme is developed, where the transmit OTFS waveform and its associated detection scheme at the receiver are jointly optimized in a deep learning framework. However, the conventional AE architecture which takes one-hot encoded input vector is hard to be reused in OTFS due to its enormous input dimensionality that increases exponentially on the number of grid points in delay-Doppler domain. To overcome it, we divide the delay-Doppler grid into multiple subblocks and associate the one-hot encoded vector with each subblock. Then, by concatenating them, one multi-hot vector is formed and exploited as the input vector for the proposed AE-based OTFS modulation and detection. We also develop a meta-learning scheme to effectively train the AE-based OTFS transceiver for newly updated channel profile.
KSP Keywords
Auto-Encoder(AE), Channel profile, Communication reliability, Deep learning framework, Detection scheme, Doppler domain, Doppler sensitivity, Meta-learning, Orthogonal frequency division Multiplexing(OFDM), deep learning(DL), delay-Doppler
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC ND)
CC BY NC ND