Estimation From Quantized Gaussian Measurements: When and How to Use Dither

Rapp, Joshua; Dawson, Robin M.; Goyal, Vivek K

doi:10.1109/tsp.2019.2916046

Cited by 7 publications

(7 citation statements)

References 61 publications

(72 reference statements)

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“…Note that the equality in the above inequality happens when ξ = ξ i . Thus, the MM iteration cycle in (11) approaches the ML estimation of ξ as long as the initial value L( ξ 0 ) is smaller than all local minima in L( ξ). Next, we recall the following lemma in [14] to construct a suitable majorizing function of L( ξ).…”

Section: Majorization-minimization Based Maximum Likelihood Estimationmentioning

confidence: 99%

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Majorization-Minimization-Based Direct Localization Using One-Bit Channel Measurements

Wang,

Tan,

Lohan

et al. 2024

IEEE Wireless Commun. Lett.

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Direct localization or direct position determination (DPD) can outperform the more traditional angle and delay estimation based approaches, yet being less used in practice due to the requirement of aggregating raw data or measurements to a single processing point. To reduce the network burden, this paper considers one-bit quantized channel response data, and proposes a majorization-minimization (MM) based one-bit DPD (MO-DPD) algorithm to localize an orthogonal frequency division multiplexing (OFDM) signal source. First, the one-bit DPD is formulated as a maximum likelihood (ML) estimation problem, which is then iteratively solved using the MM approach. The proposed MO-DPD avoids iteratively estimating any nuisance parameters, leading to high computational efficiency. The numerical results show that the MO-DPD outperforms the baseline one-bit ML solver in terms of computational load, while efficiently converging to one-bit Cramér-Rao lower bound (CRLB) over wide range of signal-to-noise ratios (SNRs). Furthermore, we show that no more than three iterations are required to achieve high accuracy.

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Section: Majorization-minimization Based Maximum Likelihood Estimationmentioning

confidence: 99%

“…One well-practiced option to develop lightweight algorithms is to apply low-bit quantization [11]- [13], including the extreme case of one-bit data [14]- [16]. Using quantized observations in DPD is a promising option for saving network traffic [17], with the one-bit processing approach recently adopted in [18].…”

Section: Introductionmentioning

confidence: 99%

Majorization-Minimization-Based Direct Localization Using One-Bit Channel Measurements

Wang,

Tan,

Lohan

et al. 2024

IEEE Wireless Commun. Lett.

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“…A recent study ( [26]) explores the use of quantization with dithering to determine which distribution the substractive dithering follows. The work presented in [27] shows that the use of dithering with quantization could be improved if an orthogonal transformation was performed on the quantizer input prior to the quantization process.…”

Section: Ditheringmentioning

confidence: 99%

Communication-efficient ADMM using Quantization-aware Gaussian Process Regression

Tudela¹,

Wei²,

Nghiem³

2022

Preprint

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<p> In networks consisting of agents communicating with a central coordinator and working together to solve a global optimization problem in a distributed manner, the agents are often required to solve private proximal minimization subproblems. Such a setting often requires a decomposition method to solve the global distributed problem, resulting in extensive communication overhead. In networks where communication is expensive, it is crucial to reduce the communication overhead of the distributed optimization scheme. Gaussian processes (GPs) are effective at learning the agents’ local proximal operators, thereby reducing the communication between the agents and the coordinator. We propose combining this learning method with adaptive uniform quantization for a hybrid approach that can achieve further communication reduction. In our approach, the GP algorithm is modified to account for the introduced quantization noise statistics due to data quantization. We further improve our approach by introducing an orthogonalization process to the quantizer’s input to address the inherent correlation of the input components. We also use dithering to ensure uncorrelation between the quantizer’s introduced noise and its input. We propose multiple measures to quantify the trade-off between the communication cost reduction and the optimization solution’s accuracy/optimality. Under such metrics, our proposed algorithms can achieve significant communication reduction for distributed optimization with acceptable accuracy, even at low quantization resolutions. This result is demonstrated by simulations of a distributed sharing problem with quadratic cost functions for the agents. </p>

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“…Rapp et al [29] demonstrated the effectiveness of a subtractive-dithered time-correlated single-photon-counting ranging system for improving the depth resolution. Next, the dithered imaging generalised Gaussian instrument response function (IRF) approximation and exponentially modified Gaussian IRF model provided accurate ranging results from sub-bin delayed imaging [30][31][32]. Chang et al [33] achieved kilometre-scale dither superresolution depth imaging.…”

Section: Introductionmentioning

confidence: 99%

Sub-Bin Delayed High-Range Accuracy Photon-Counting 3D Imaging

Yin,

Zhao,

Yang

et al. 2024

Photonics

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The range accuracy of single-photon-array three-dimensional (3D) imaging systems is limited by the time resolution of the array detectors. We introduce a method for achieving super-resolution in 3D imaging through sub-bin delayed scanning acquisition and fusion. Its central concept involves the generation of multiple sub-bin difference histograms through sub-bin shifting. Then, these coarse time-resolution histograms are fused with multiplied averages to produce finely time-resolved detailed histograms. Finally, the arrival times of the reflected photons with sub-bin resolution are extracted from the resulting fused high-time-resolution count distribution. Compared with the sub-delayed with the fusion method added, the proposed method performs better in reducing the broadening error caused by coarsened discrete sampling and background noise error. The effectiveness of the proposed method is examined at different target distances, pulse widths, and sub-bin scales. The simulation analytical results indicate that small-scale sub-bin delays contribute to superior reconstruction outcomes for the proposed method. Specifically, implementing a sub-bin temporal resolution delay of a factor of 0.1 for a 100 ps echo pulse width substantially reduces the system ranging error by three orders of magnitude. Furthermore, Monte Carlo simulations allow to describe a low signal-to-background noise ratio (0.05) characterised by sparsely reflected photons. The proposed method demonstrates a commendable capability to simultaneously achieve wide-ranging super-resolution and denoising. This is evidenced by the detailed depth distribution information and substantial reduction of 95.60% in the mean absolute error of the reconstruction results, confirming the effectiveness of the proposed method in noisy scenarios.

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Estimation From Quantized Gaussian Measurements: When and How to Use Dither

Cited by 7 publications

References 61 publications

Majorization-Minimization-Based Direct Localization Using One-Bit Channel Measurements

Majorization-Minimization-Based Direct Localization Using One-Bit Channel Measurements

Communication-efficient ADMM using Quantization-aware Gaussian Process Regression

Sub-Bin Delayed High-Range Accuracy Photon-Counting 3D Imaging

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