Antithetic Dithered 1-Bit Massive MIMO Architecture: Efficient Channel Estimation via Parameter Expansion and PML

Ho, David K. W.; Rao, Bhaskar D.

doi:10.1109/tsp.2019.2903016

Cited by 11 publications

(3 citation statements)

References 23 publications

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“…Assume that each node is equipped with a random generator (RG) and a quantizer. At time , each RG can provide a zero-mean random dithering [20], [21] with variance . Using , the quantizer can produce a low-resolution multi-bit output , , as shown in Figure 1, where…”

Section: Problem Formulationmentioning

confidence: 99%

Graph Signal Reconstruction from Low-Resolution Multi-Bit Observations

Liu,

Yu,

Wang

et al. 2024

Chinese J. Elect.

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Low hardware cost and power consumption in information transmission, processing and storage is an urgent demand for many big data problems, in which the high-dimensional data often be modelled as graph signals. This paper considers the problem of recovering a smooth graph signal by using its low-resolution multi-bit quantized observations. The underlying problem is formulated as a regularized maximum-likelihood optimization and is solved via an expectation maximization scheme. With this scheme, the multi-bit graph signal recovery (MB-GSR) is efficiently implemented by using the quantized observations collected from random subsets of graph nodes. The simulation results show that increasing the sampling resolution to 2 or 3 bits per sample leads to a considerable performance improvement, while the energy consumption and implementation costs remain much lower compared to the implementation of high resolution sampling.

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Section: Problem Formulationmentioning

confidence: 99%

Graph Signal Reconstruction from Low-Resolution Multi-Bit Observations

Liu,

Yu,

Wang

et al. 2024

Chinese J. Elect.

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show abstract

“…Sampling at high data rates with high resolution ADCs, however, would dramatically increase the overall power consumption and the manufacturing cost of such ADCs [13]. This problem is exacerbated in systems that require multiple ADCs such as large array receivers [14]. An immediate solution to such challenges is to use fewer bits for sampling.…”

Section: Introductionmentioning

confidence: 99%

UNO: Unlimited Sampling Meets One-Bit Quantization

Eamaz,

Mishra,

Yeganegi

et al. 2024

IEEE Trans. Signal Process.

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Recent results in one-bit sampling provide a framework for a relatively low-cost, low-power sampling, at a high rate by employing time-varying sampling threshold sequences. Another recent development in sampling theory is unlimited sampling, which is a high-resolution technique that relies on modulo ADCs to yield an unlimited dynamic range. In this paper, we leverage the appealing attributes of the two aforementioned techniques to propose a novel unlimited one-bit (UNO) sampling approach. In this framework, the information on the distance between the input signal value and the threshold is stored and utilized to accurately reconstruct the one-bit sampled signal. We then utilize this information to accurately reconstruct the signal from its one-bit samples via the randomized Kaczmarz algorithm (RKA). In the presence of noise, we employ the recent plug-and-play (PnP) priors technique with alternating direction method of multipliers (ADMM) to exploit integration of stateof-the-art regularizers in the reconstruction process. Numerical experiments with RKA and PnP-ADMM-based reconstruction illustrate the effectiveness of our proposed UNO, including its superior performance compared to the one-bit Σ∆ sampling.

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“…Sampling at high data rates with high resolution ADCs, however, would dramatically increase the overall power consumption and the manufacturing cost of such ADCs [24]. This problem is exacerbated in systems that require multiple ADCs such as large array receivers [25]. An immediate solution to such challenges is to use fewer bits for sampling.…”

Section: Introductionmentioning

confidence: 99%

UNO: Unlimited Sampling Meets One-Bit Quantization

Eamaz¹,

Mishra²,

Yeganegi³

et al. 2023

Preprint

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Recent results in one-bit sampling provide a framework for a relatively low-cost, low-power sampling, at a high rate by employing time-varying sampling threshold sequences. Another recent development in sampling theory is unlimited sampling, which is a high-resolution technique that relies on self-reset ADCs to yield an unlimited dynamic range. In this paper, we leverage the appealing attributes of the two aforementioned techniques to propose a novel unlimited one-bit (UNO) sampling approach. In this framework, the information on the distance between the input signal value and the threshold are stored and utilized to accurately reconstruct the one-bit sampled signal. We then utilize this information to accurately reconstruct the signal from its one-bit samples via the randomized Kaczmarz algorithm (RKA); a strong linear feasibility solver that selects a random linear equation at each iteration. In the presence of noise, we employ the recent plug-and-play (PnP) priors technique with alternating direction method of multipliers (ADMM) to exploit integration of state-of-the-art regularizers in the reconstruction process. Numerical experiments with RKA and PnP-ADMM-based reconstruction illustrate the effectiveness of our proposed UNO, including its superior performance compared to the one-bit Σ∆ sampling.

show abstract

Antithetic Dithered 1-Bit Massive MIMO Architecture: Efficient Channel Estimation via Parameter Expansion and PML

Cited by 11 publications

References 23 publications

Graph Signal Reconstruction from Low-Resolution Multi-Bit Observations

Graph Signal Reconstruction from Low-Resolution Multi-Bit Observations

UNO: Unlimited Sampling Meets One-Bit Quantization

UNO: Unlimited Sampling Meets One-Bit Quantization

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