Signal Parameter Estimation Using 1-Bit Dithered Quantization

Dabeer, Onkar; Karnik, Aditya

doi:10.1109/tit.2006.885461

Cited by 90 publications

(114 citation statements)

References 17 publications

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“…Such problems can be cast in the more general framework of parameter estimation in the presence of quantized output measurements [25]- [30]. The specific case of channel estimation has been discussed in [31], where the maximum-likelihood (ML) estimate is computed via expectation maximization.…”

Section: B Relevant Prior Artmentioning

confidence: 99%

Quantized Massive MU-MIMO-OFDM Uplink

Studer

Durisi

2016

IEEE Trans. Commun.

229

223

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Abstract-Coarse quantization at the base station (BS) of a massive multi-user (MU) multiple-input multiple-output (MIMO) wireless system promises significant power and cost savings. Coarse quantization also enables significant reductions of the raw analog-to-digital converter (ADC) data that must be transferred from a spatially-separated antenna array to the baseband processing unit. The theoretical limits as well as practical transceiver algorithms for such quantized MU-MIMO systems operating over frequency-flat, narrowband channels have been studied extensively. However, the practically relevant scenario where such communication systems operate over frequency-selective, wideband channels is less well understood. This paper investigates the uplink performance of a quantized massive MU-MIMO system that deploys orthogonal frequency-division multiplexing (OFDM) for wideband communication. We propose new algorithms for quantized maximum a-posteriori (MAP) channel estimation and data detection, and we study the associated performance/quantization trade-offs. Our results demonstrate that coarse quantization (e.g., four to six bits, depending on the ratio between the number of BS antennas and the number of users) in massive MU-MIMO-OFDM systems entails virtually no performance loss compared to the infinite-precision case at no additional cost in terms of baseband processing complexity.Index Terms-Analog-to-digital conversion, convex optimization, forward-backward splitting (FBS), frequency-selective channels, massive multi-user multiple-input multiple-output (MU-MIMO), maximum a-posteriori (MAP) channel estimation, minimum mean-square error (MMSE) data detection, orthogonal frequency-division multiplexing (OFDM), quantization.

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Section: B Relevant Prior Artmentioning

confidence: 99%

Quantized Massive MU-MIMO-OFDM Uplink

Studer

Durisi

2016

IEEE Trans. Commun.

229

223

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“…The following corollary shows that the situation simplifies when the likelihood function is symmetric. This can be seen as a generalization of [5].…”

Section: Theorem 2 (Quantized Likelihood Function)mentioning

confidence: 97%

“…Information is thus lost, but it is at least not mis-interpreted leading to an estimation bias. For the case of parameter estimation in 1-bit quantized samples, [5,6] design the dithering noise by MSE optimization, trading off a bias decrease to the variance increase due to dithering. Adding a suitably designed dithering noise should simplify the derivation of estimators.…”

Section: Introductionmentioning

confidence: 99%

Generating dithering noise for maximum likelihood estimation from quantized data

Gustafsson

Karlsson²

2013

Automatica

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The Quantization Theorem I (QT I) implies that the likelihood function can be reconstructed from quantized sensor observations, given that appropriate dithering noise is added before quantization. We present constructive algorithms to generate such dithering noise. The application to maximum likelihood estimation (mle) is studied in particular. In short, dithering has the same role for amplitude quantization as an anti-alias filter has for sampling, in that it enables perfect reconstruction of the dithered but unquantized signal's likelihood function. Without dithering, the likelihood function suffers from a kind of aliasing expressed as a counterpart to Poisson's summation formula which makes the exact mle intractable to compute. With dithering, it is demonstrated that standard mle algorithms can be re-used on a smoothed likelihood function of the original signal, and statistically efficiency is obtained. The implication of dithering to the Cramér-Rao Lower Bound (CRLB) is studied, and illustrative examples are provided.

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“…Both types of dither noise are considered in this paper. Motivated by the satisfactory performance of the uniform dithering in [6], uniformly distributed dither noise is considered as well. For each dither noise type, we derive the corresponding likelihood function in the sequel.…”

Section: B Mle With Ditheringmentioning

confidence: 99%

Dithering in quantized RSS based localization

Jin

Zoubir

Yin

et al. 2015

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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Abstract-We study maximum likelihood (ML) position estimation using quantized received signal strength measurements. In order to mitigate the undesired quantization effect in the observations, the dithering technique is adopted. Various dither noise distributions are considered and the corresponding likelihood functions are derived. Simulation results show that the proposed ML estimator with dithering is able to generate a significantly reduced bias but a modestly increased mean-squareerror as compared to the conventional ML estimator without dithering.

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Signal Parameter Estimation Using 1-Bit Dithered Quantization

Cited by 90 publications

References 17 publications

Quantized Massive MU-MIMO-OFDM Uplink

Quantized Massive MU-MIMO-OFDM Uplink

Generating dithering noise for maximum likelihood estimation from quantized data

Dithering in quantized RSS based localization

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