Orthogonal Time–Frequency Space Modulation: Principles and Implementation

Farhang, Arman; Farhang‐Boroujeny, Behrouz

doi:10.1002/9781119652434.ch6

Cited by 2 publications

(1 citation statement)

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“…To tackle the Doppler sensitivity problem of the OFDM, orthogonal time frequency space (OTFS) modulation has been investigated [9], [10], [11], [12] (and references therein), where information-carrying quadrature amplitude modulation (QAM) symbols are modulated in two dimensional (2D) delay-Doppler domain. Note that the channel variation is relatively smaller in the delay-Doppler domain than in the time-frequency domain [9], [13].…”

Section: Introductionmentioning

confidence: 99%

Auto-Encoder Based Orthogonal Time Frequency Space Modulation and Detection With Meta-Learning

et al. 2023

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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.INDEX TERMS Hierarchical auto-encoder, meta-learning strategy, OTFS waveform.

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