Downlink Synchronization for OTFS-Based Cellular Systems in High Doppler Environments

Khan, Mohammed Saquib; Kim, Yeong Jun; Sultan, Qasim; Joung, Jingon; Cho, Yong Soo

doi:10.1109/access.2021.3079429

Cited by 17 publications

(10 citation statements)

References 31 publications

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“…Orthogonal time-frequency-spread (OTFS) is essential in 5G mmWave to provide less BER, high reliability, and flexibility for pilot design in a noisy environment. OTFS transforms the fading medium and time-varying characteristics of the wireless channel into nonfading and time-dependent wireless channels [209,210].…”

Section: Signaling Techniques For 5gmentioning

confidence: 99%

Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

et al. 2021

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Fifth-generation (5G) technology will play a vital role in future wireless networks. The breakthrough 5G technology will unleash a massive Internet of Everything (IoE), where billions of connected devices, people, and processes will be simultaneously served. The services provided by 5G include several use cases enabled by the enhanced mobile broadband, massive machine-type communications, and ultra-reliable low-latency communication. Fifth-generation networks potentially merge multiple networks on a single platform, providing a landscape for seamless connectivity, particularly for high-mobility devices. With their enhanced speed, 5G networks are prone to various research challenges. In this context, we provide a comprehensive survey on 5G technologies that emphasize machine learning-based solutions to cope with existing and future challenges. First, we discuss 5G network architecture and outline the key performance indicators compared to the previous and upcoming network generations. Second, we discuss next-generation wireless networks and their characteristics, applications, and use cases for fast connectivity to billions of devices. Then, we confer physical layer services, functions, and issues that decrease the signal quality. We also present studies on 5G network technologies, 5G propelling trends, and architectures that help to achieve the goals of 5G. Moreover, we discuss signaling techniques for 5G massive multiple-input and multiple-output and beam-forming techniques to enhance data rates with efficient spectrum sharing. Further, we review security and privacy concerns in 5G and standard bodies’ actionable recommendations for policy makers. Finally, we also discuss emerging challenges and future directions.

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Section: Signaling Techniques For 5gmentioning

confidence: 99%

Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

et al. 2021

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“…[4]. In [5], the authors developed a correlation-based method for TO estimation in OTFS downlink transmission, which uses a preamble consisting of a linear frequency-modulated (LFM) waveform and two OTFS symbols. Due to the rapid variation of the channel in high mobility scenarios, the TO estimated at the base station using the preamble would be outdated during the data transmission phase.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the rapid variation of the channel in high mobility scenarios, the TO estimated at the base station using the preamble would be outdated during the data transmission phase. Hence, the methods developed in [4] and [5] are not suitable for high-mobility scenarios, and CFO estimation is not addressed in either [4] or [5].…”

Section: Introductionmentioning

confidence: 99%

Practical Synchronization for OTFS

Bayat¹,

S.²,

Farhang³

2023

Preprint

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In the existing literature on joint timing and frequency synchronization of orthogonal time frequency space modulation (OTFS), practically infeasible impulse pilot with large peak-to-average power ratio (PAPR) is deployed. Hence, in this paper, we propose a timing offset (TO) and carrier frequency offset (CFO) estimation for OTFS over a linear timevarying (LTV) channel, using a low PAPR pilot structure. The proposed technique utilizes the recently proposed practically feasible pilot structure with a cyclic prefix (PCP). We exploit the periodic properties of PCP in both delay and time domains to find the starting point of each OTFS block. Furthermore, we propose a two-stage CFO estimation technique with over an order of magnitude higher estimation accuracy than the existing estimator using the impulse pilot. In the first stage, a coarse CFO estimate is obtained which is refined in the second stage, through our proposed maximum likelihood (ML) based approach. The proposed ML-based approach deploys the generalized complex exponential basis expansion model (GCE-BEM) to capture the time variations of the channel, absorb them into the pilot and provide an accurate CFO estimate. Since our proposed synchronization technique utilizes the same pilot deployed for channel estimation, it does not require any additional overhead. Finally, we evaluate the performance of our proposed synchronization technique through simulations. We also compare and show the superior performance of our proposed technique to the only other existing joint TO and CFO estimation method in OTFS literature.

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“…However, these TO reports may become outdated. Thus, the authors in [10], proposed a preamble-based TO and cell identity estimation method in downlink. In this method, a linear frequency modulated (LFM) signal is utilized as a preamble.…”

Section: Introductionmentioning

confidence: 99%

“…In this method, a linear frequency modulated (LFM) signal is utilized as a preamble. Both solutions in [9] and [10] are limited to time synchronization and the only work on the frequency synchronization of OTFS can be found in [11]. In this work, the CFO effect is considered as a part of the channel and a joint CFO and channel estimation method using an isolated pilot in the delay-Doppler domain is proposed.…”

Section: Introductionmentioning

confidence: 99%

Time and Frequency Synchronization for OTFS

Bayat

Farhang

2022

IEEE Wireless Commun. Lett.

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In this letter, we propose timing offset (TO) and carrier frequency offset (CFO) estimators for orthogonal time frequency space modulation (OTFS). The proposed estimators do not require any additional training overheads as they deploy the same pilot signal that is used for channel estimation. Hence, no additional training overhead is required for synchronization, thanks to the periodic properties of the pilot signal in the delaytime domain. Our proposed TO estimator finds the start of each OTFS block by searching for a periodic sequence in delay and time dimensions. The CFO is then estimated by finding the mean of the angles of a two-dimensional correlation function at the best timing instant. As a novel aspect of these estimators, the multipath diversity of the channel is exploited to achieve a high estimation accuracy. Finally, we show the efficacy of our proposed synchronization techniques through extensive simulations.

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Downlink Synchronization for OTFS-Based Cellular Systems in High Doppler Environments

Cited by 17 publications

References 31 publications

Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

Practical Synchronization for OTFS

Time and Frequency Synchronization for OTFS

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