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The orthogonal time frequency space (OTFS) symbol detector design for highmobility communication scenarios has received numerous attention lately.Current state-of-the-art OTFS detectors mainly can be divided into twocategories; iterative and training-based deep neural network (DNN) detectors.Many practical iterative detectors rely on minimum-mean-square-error (MMSE)denoiser to get the initial symbol estimates. However, their computationalcomplexity increases exponentially with the number of detected symbols.Training-based DNN detectors typically suffer from dependency on theavailability of large computation resources and the fidelity of syntheticdatasets for the training phase, which are both costly. In this paper, wepropose an untrained DNN based on the deep image prior (DIP) and decoderarchitecture, referred to as D-DIP that replaces the MMSE denoiser in theiterative detector. DIP is a type of DNN that requires no training, which makesit beneficial in OTFS detector design. Then we propose to combine the D-DIPdenoiser with the Bayesian parallel interference cancellation (BPIC) detectorto perform iterative symbol detection, referred to as D-DIP-BPIC. Oursimulation results show that the symbol error rate (SER) performance of theproposed D-DIP-BPIC detector outperforms practical state-of-the-art detectorsby 0.5 dB and retains low computational complexity.

Untrained Neural Network based Bayesian Detector for OTFS Modulation Systems