Sparse Bayesian Learning of Delay-Doppler Channel for OTFS System

Zhao, Lei; Gao, Wenjing; Guo, Wenbin

doi:10.1109/lcomm.2020.3021120

Cited by 96 publications

(58 citation statements)

References 9 publications

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“…Alternatively, the innovative contributions [15], [21], [23], [26], [27] exploit the DD-domain sparsity of the wireless channel by conceiving an interesting formulation of the DDdomain CSI estimation model as a sparse signal recovery problem. These schemes have demonstrated superior CSI estimation performance in comparison to the previously discussed training impulse and embedded pilot techniques, since they leverage the sparsity of the underlying DD-domain channel.…”

Section: A Review Of Existing Contributions On Otfs Csi Estimationmentioning

confidence: 99%

“…The authors then suitably adapt the OMP and modified subspace pursuit (MSP) algorithms for sparse CSI estimation. To this end, Zhao et al [21], proposed a novel pilot pattern, characterized by the absence of a DDdomain-guard band between the pilots and data. This pilotdata frame structure is successfully exploited in their work to formulate a sparse channel estimation problem for SISO OTFS systems.…”

Section: A Review Of Existing Contributions On Otfs Csi Estimationmentioning

confidence: 99%

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Bayesian Learning Aided Simultaneous Row and Group Sparse Channel Estimation in Orthogonal Time Frequency Space Modulated MIMO Systems

Srivastava

Singh

Jagannatham

et al. 2022

IEEE Trans. Commun.

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A sparse channel state information (CSI) estimation model is proposed for reducing the pilot overhead of orthogonal time frequency space (OTFS) modulation aided multipleinput multiple-output (MIMO) systems. Explicitly, the pilots are directly transmitted over the time-frequency (TF)-domain grid for estimating the delay-Doppler (DD)-domain CSI that leads to a reduction of the pilot overhead, training duration and pre-processing complexity. Furthermore, it completely avoids placing multiple DD-domain guard intervals corresponding to each transmit antenna within the same OTFS frame, while keeping the training duration flexible, hence increasing the bandwidth efficiency. A unique benefit of the proposed CSI estimation model is that it can efficiently handle fractional Dopplers also. The resultant DD-domain CSI becomes simultaneously row and group (RG)-sparse. To exploit this compelling property, an orthogonal matching pursuit (OMP)-based RG-OMP technique is developed, conveniently complemented by an enhanced Bayesian learning (BL)-based RG-BL framework, both of which substantially outperform the state-of-the-art methods. Furthermore, low-complexity linear detectors are designed for the ensuing data detection phase, which directly employ the estimated DD-domain sparse CSI, without assuming any further knowledge concerning the number of dominant multipath components. Finally, simulation results are provided to demonstrate performance improvement of the proposed BL-based schemes over the OMP and the state-of-the-art schemes.

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Section: A Review Of Existing Contributions On Otfs Csi Estimationmentioning

confidence: 99%

Section: A Review Of Existing Contributions On Otfs Csi Estimationmentioning

confidence: 99%

Bayesian Learning Aided Simultaneous Row and Group Sparse Channel Estimation in Orthogonal Time Frequency Space Modulated MIMO Systems

Srivastava

Singh

Jagannatham

et al. 2022

IEEE Trans. Commun.

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“…For OTFS based single-antenna systems (where there is no additional sparsity due to multiple antennas), recently a parametric channel estimation approach has been considered in [19]- [21], where instead of estimating the elements of the DD domain channel matrix, the channel path parameters (i.e., path gain, path delay and Doppler shifts) are estimated from the received pilot signals. The effective DD domain channel matrix can then be reconstructed from the estimated channel path parameters.…”

Section: Introductionmentioning

confidence: 99%

“…As the number of channel path parameters is usually much smaller than the number of significant energy elements of the effective DD domain channel matrix, these parameters can be estimated effectively (without the need for additional sparsity) based on the sparse Bayesian learning (SBL) method [22], [23]. SBL based OTFS channel estimation has been considered in [19]- [21].…”

Section: Introductionmentioning

confidence: 99%

“…Although [19], [20] propose an SBL based OTFS channel estimation method, they consider the channel path delays to be integer multiples of the delay domain resolution which is not a practical assumption for realistic scenarios. The SBL based OTFS channel estimation in [21] considers non-integer delay and Doppler shifts and is shown to achieve good channel estimation accuracy in single-antenna systems.…”

Section: Introductionmentioning

confidence: 99%

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Low Complexity Channel Estimation for OTFS Modulation with Fractional Delay and Doppler

Khan¹,

Mohammed²

2021

Preprint

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We consider the problem of accurate channel estimation for OTFS based systems with few transmit/receive antennas, where additional sparsity due to large number of antennas is not a possibility. For such systems the sparsity of the effective delay-Doppler (DD) domain channel is adversely affected in the presence of channel path delay and Doppler shifts which are noninteger multiples of the delay and Doppler domain resolution. The sparsity is also adversely affected when practical transmit and receive pulses are used. In this paper we propose a Modified Maximum Likelihood Channel Estimation (M-MLE) method for OTFS based systems which exploits the fine delay and Doppler domain resolution of the OTFS modulated signal to decouple the joint estimation of the channel parameters (i.e., channel gain, delay and Doppler shift) of all channel paths into separate estimation of the channel parameters for each path. We further observe that with fine delay and Doppler domain resolution, the received DD domain signal along a particular channel path can be written as a product of a delay domain term and a Doppler domain term where the delay domain term is primarily dependent on the delay of this path and the Doppler domain term is primarily dependent on the Doppler shift of this path. This allows us to propose another method termed as the twostep method (TSE), where the joint two-dimensional estimation of the delay and Doppler shift of a particular path in the M-MLE method is further decoupled into two separate one-dimensional estimation for the delay and for the Doppler shift of that path. Simulations reveal that the proposed methods (M-MLE and TSE) achieve better channel estimation accuracy at lower complexity when compared to other known methods for accurate OTFS channel estimation.

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References

2024

OTFS Modulation ‐ Theory and Applications

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Sparse Bayesian Learning of Delay-Doppler Channel for OTFS System

Cited by 96 publications

References 9 publications

Bayesian Learning Aided Simultaneous Row and Group Sparse Channel Estimation in Orthogonal Time Frequency Space Modulated MIMO Systems

Bayesian Learning Aided Simultaneous Row and Group Sparse Channel Estimation in Orthogonal Time Frequency Space Modulated MIMO Systems

Low Complexity Channel Estimation for OTFS Modulation with Fractional Delay and Doppler

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