Low-complexity Delay-Doppler Symbol DNN for OTFS Signal Detection

Naikoti, A and Chockalingam, A (2021) Low-complexity Delay-Doppler Symbol DNN for OTFS Signal Detection. In: 93rd IEEE Vehicular Technology Conference, VTC 2021-Spring, 25-28 Apr 2021.

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Abstract

In this paper, we consider the problem of low-complexity detection of orthogonal time frequency space (OTFS) modulation signals using deep neural networks (DNN). We consider a DNN architecture in which each symbol multiplexed in the delay-Doppler grid is associated with a separate DNN. The considered symbol-level DNN has fewer parameters to learn compared to a full DNN that takes into account all symbols in an OTFS frame jointly, and therefore has less complexity. Under the assumption of static multipath channel with i.i.d. Gaussian noise, our simulation results show that the performance of the symbol-DNN detection is quite close to that of the full-DNN detection as well as the maximum-likelihood (ML) detection. Further, when the noise model deviates from the standard i.i.d. Gaussian model (e.g., non-Gaussian noise with t-distribution), because of its ability to learn the distribution, the symbol-DNN detection is found to perform better than the ML detection. A similar performance advantage is observed in multiple-input multiple-output OTFS (MIMO-OTFS) where the noise across multiple received antennas are correlated. © 2021 IEEE.

Item Type:	Conference Paper
Publication:	IEEE Vehicular Technology Conference
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Complex networks; Deep neural networks; Gaussian distribution; Maximum likelihood; MIMO systems; Signal detection; Trellis codes, Gaussian model; Low-complexity detections; Maximum-likelihood detection; Modulation signals; Noise modeling; Non-Gaussian noise; T distribution; Time-frequency space, Gaussian noise (electronic)
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	20 Nov 2021 12:41
Last Modified:	20 Nov 2021 12:41
URI:	http://eprints.iisc.ac.in/id/eprint/69910

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