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Bayesian-based Symbol Detector for Orthogonal Time Frequency Space Modulation Systems
Our simulation results show that in contrast to the state-of-the-art OTFS detectors, the proposed detector is able to achieve a BER of less than $10^{-5}$, when SNR is over $14$ dB, under high ICI environments.
Bayesian Neural Network Detector for an Orthogonal Time Frequency Space Modulation
The orthogonal time-frequency space (OTFS) modulation is proposed for beyond 5G wireless systems to deal with high mobility communications.
Untrained Neural Network based Bayesian Detector for OTFS Modulation Systems
In this paper, we propose an untrained DNN based on the deep image prior (DIP) and decoder architecture, referred to as D-DIP that replaces the MMSE denoiser in the iterative detector.
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