A DNN-based OTFS Transceiver with Delay-Doppler Channel Training and IQI Compensation

Naikoti, Ashwitha; Chockalingam, A.

doi:10.1109/pimrc50174.2021.9569634

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…Additionally, in [192], authors have presented a novel DNNbased framework for the equalization of both SISO and MIMO-OTFS systems. DNN-based transceiver architectures have also been introduced for the detection of OTFS signals under IQ imbalance [193] and PAPR reduction [194]. Lastly, DL-aided receivers are presented for GFDM and GFDM-IM systems [195], [196].…”

Section: A Transceiver Designmentioning

confidence: 99%

“…[188] DL-Based GAMP Unfolding OTFS [189] N/A DNN OTFS [190] Symbol-DNN DNN OTFS [191] N/A CNN OTFS [192] N/A RNN OTFS [193] N/A DNN OTFS [194] N/A DNN OTFS only by extensive simulation and also by employing a real communication system. Another model-driven DL method based on OAMP algorithm has also been proposed for CP removal in MIMO-OFDM systems [207].…”

Section: Reference Name Ce Sd Nn Type Waveformmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

IEEE Open J. Commun. Soc.

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Deep learning (DL) has proven its unprecedented success in diverse fields such as computer vision, natural language processing, and speech recognition by its strong representation ability and ease of computation. As we move forward to a thoroughly intelligent society with 6G wireless networks, new applications and use cases have been emerging with stringent requirements for next-generation wireless communications. Therefore, recent studies have focused on the potential of DL approaches in satisfying these rigorous needs and overcoming the deficiencies of existing model-based techniques. The main objective of this article is to unveil the state-of-the-art advancements in the field of DL-based physical layer methods to pave the way for fascinating applications of 6G. In particular, we have focused our attention on four promising physical layer concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output systems, sophisticated multi-carrier waveform designs, reconfigurable intelligent surface-empowered communications, and physical layer security. We examine up-to-date developments in DL-based techniques, provide comparisons with state-of-the-art methods, and introduce a comprehensive guide for future directions. We also present an overview of the underlying concepts of DL, along with the theoretical background of well-known DL techniques. Furthermore, this article provides programming examples for a number of DL techniques and the implementation of a DLbased multiple-input multiple-output by sharing user-friendly code snippets, which might be useful for interested readers.

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Section: A Transceiver Designmentioning

confidence: 99%

Section: Reference Name Ce Sd Nn Type Waveformmentioning

confidence: 99%

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

show abstract

“…For using DL in OTFS systems, Naikoti et al conducted a preliminary exploration of FC-DNN-based signal detection [30]. Li et al proposed a receiver with CNN-based signal detection for OTFS [31].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication

Zhang

Wang

et al. 2022

JMSE

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Orthogonal time frequency space (OTFS) is a novel two-dimensional (2D) modulation technique that provides reliable communications over time- and frequency-selective channels. In underwater acoustic (UWA) channel, the multi-path delay and Doppler shift are several magnitudes larger than wireless radio communication, which will cause severe time- and frequency-selective fading. The receiver has to recover the distorted OTFS signal with inter-symbol interference (ISI) and inter-carrier interference (ICI). The conventional UWA OTFS receivers perform channel estimation explicitly and equalization to detect transmitted symbols, which requires prior knowledge of the system. This paper proposes a deep learning-based signal detection method for UWA OTFS communication, in which the deep neural network can recover the received symbols after sufficient training. In particular, it cascades a convolutional neural network (CNN) with skip connections (SC) and a bidirectional long short-term memory (BiLSTM) network to perform signal recovery. The proposed method extracts feature information from received OTFS signal sequences and trains the neural network for signal detection. The numerical results demonstrate that the SC-CNN-BiLSTM-based OTFS detection method performs with a lower bit error rate (BER) than the 2D-CNN, FC-DNN, and conventional signal detection methods.

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“…The I/Q imbalance impairments were discussed in [ 26 , 27 ]; it was demonstrated that the BER performance of the OTFS systems is worsened by I/Q imbalance impairments and has to be compensated. The authors in [ 28 ] use a deep neural network (DNN)-based technique to deal with I/Q imbalance. In [ 29 ], the authors provide a mathematical study of the I/Q imbalance as well as the direct current (DC) offset.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Experimental Emulation for OTFS Waveform RF-Impairments

Abushattal

Zegrar

Yazgan

et al. 2022

Sensors

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The orthogonal time-frequency space (OTFS) waveform exceeds the challenges that face orthogonal frequency division multiplexing (OFDM) in a high-mobility environment with high time-frequency dispersive channels. Since radio frequency (RF) impairments have a direct impact on waveform behavior, this paper investigates the experimental implementation of RF-impairments that affect OTFS waveform performance and compares them to the OFDM waveform as a benchmark. Firstly, the doubly-dispersive channel effect is analyzed, and then an experimental framework is established for investigating the impact of RF-impairments, including non-linearity, carrier frequency offset (CFO), I/Q imbalances, DC-offset, and phase noise are considered. The experiments were conducted in a real indoor wireless environment using software-defined radio (SDR) at carrier frequencies of 2.4 GHz and 5 GHz based on the Keysight EXG X-Series devices. The comparison of the performances of OFDM and OTFS in the presence of RF-impairments reveals that OTFS significantly outperforms OFDM.

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A DNN-based OTFS Transceiver with Delay-Doppler Channel Training and IQI Compensation

Cited by 13 publications

References 18 publications

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication

A Comprehensive Experimental Emulation for OTFS Waveform RF-Impairments

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