56,371 research outputs found
Non-Data-Aided Parameter Estimation in an Additive White Gaussian Noise Channel
Non-data-aided (NDA) parameter estimation is considered for
binary-phase-shift-keying transmission in an additive white Gaussian noise
channel. Cramer-Rao lower bounds (CRLBs) for signal amplitude, noise variance,
channel reliability constant and bit-error rate are derived and it is shown how
these parameters relate to the signal-to-noise ratio (SNR). An alternative
derivation of the iterative maximum likelihood (ML) SNR estimator is presented
together with a novel, low complexity NDA SNR estimator. The performance of the
proposed estimator is compared to previously suggested estimators and the CRLB.
The results show that the proposed estimator performs close to the iterative ML
estimator at significantly lower computational complexity
Superimposed Pilots are Superior for Mitigating Pilot Contamination in Massive MIMO
In this paper, superimposed pilots are introduced as an alternative to time-multiplexed pilot and data symbols for mitigating pilot contamination in massive multiple-input multiple-output (MIMO) systems. We propose a non-iterative scheme for uplink channel estimation based on superimposed pilots and derive an expression for the uplink signal-to-interference-plus-noise ratio (SINR) at the output of a matched filter employing this channel estimate. Based on this expression, we observe that power control is essential when superimposed pilots are employed. Moreover, the quality of the channel estimate can be improved by reducing the interference that results from transmitting data alongside the pilots, and an intuitive iterative data-aided scheme that reduces this component of interference is also proposed. Approximate expressions for the uplink SINR are provided for the iterative data-aided method as well. In addition, we show that a hybrid system with users utilizing both time-multiplexed and superimposed pilots is superior to an optimally designed system that employs only time-multiplexed pilots, even when the non-iterative channel estimate is used to build the detector and precoder. We also describe a simple approach to implement this hybrid system by minimizing the overall inter and intra-cell interference. Numerical simulations demonstrating the performance of the proposed channel estimation schemes and the superiority of the hybrid system are also provided
Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal
Objective: Magnetic Resonance Spectroscopy (MRS) is an important technique
for biomedical detection. However, it is challenging to accurately quantify
metabolites with proton MRS due to serious overlaps of metabolite signals,
imperfections because of non-ideal acquisition conditions, and interference
with strong background signals mainly from macromolecules. The most popular
method, LCModel, adopts complicated non-linear least square to quantify
metabolites and addresses these problems by designing empirical priors such as
basis-sets, imperfection factors. However, when the signal-to-noise ratio of
MRS signal is low, the solution may have large deviation. Methods: Linear Least
Squares (LLS) is integrated with deep learning to reduce the complexity of
solving this overall quantification. First, a neural network is designed to
explicitly predict the imperfection factors and the overall signal from
macromolecules. Then, metabolite quantification is solved analytically with the
introduced LLS. In our Quantification Network (QNet), LLS takes part in the
backpropagation of network training, which allows the feedback of the
quantification error into metabolite spectrum estimation. This scheme greatly
improves the generalization to metabolite concentrations unseen for training
compared to the end-to-end deep learning method. Results: Experiments show that
compared with LCModel, the proposed QNet, has smaller quantification errors for
simulated data, and presents more stable quantification for 20 healthy in vivo
data at a wide range of signal-to-noise ratio. QNet also outperforms other
end-to-end deep learning methods. Conclusion: This study provides an
intelligent, reliable and robust MRS quantification. Significance: QNet is the
first LLS quantification aided by deep learning
A performance lower bound for quadratic timing recovery accounting for the symbol transition density
The symbol transition density in a digitally modulated signal affects the performance of practical synchronization schemes designed for timing recovery. This paper focuses on the derivation of simple performance limits for the estimation of the time delay of a noisy linearly modulated signal in the presence of various degrees of symbol correlation produced by the various
transition densities in the symbol streams. The paper develops high- and low-signal-to-noise ratio (SNR) approximations of the so-called (Gaussian) unconditional Cramér–Rao bound (UCRB),
as well as general expressions that are applicable in all ranges of SNR. The derived bounds are valid only for the class of quadratic, non-data-aided (NDA) timing recovery schemes. To illustrate the validity of the derived bounds, they are compared with the actual performance achieved by some well-known quadratic NDA timing recovery schemes. The impact of the symbol transition
density on the classical threshold effect present in NDA timing recovery schemes is also analyzed. Previous work on performance
bounds for timing recovery from various authors is generalized and unified in this contribution.Peer Reviewe
Visualization on colour based flow vector of thermal image for movement detection during interactive session
Recently thermal imaging is exploited in applications such as motion and face detection. It has drawn attention many researchers to build such technology to improve lifestyle. This work proposed a technique to detect and identify a motion in sequence images for the application in security monitoring system or outdoor surveillance. Conventional system might cause false information with the present of shadow. Thus, methods employed in this work are Canny edge detector method, Lucas Kanade and Horn Shunck algorithms, to overcome the major problem when using thresholding method, which is only intensity or pixel magnitude is considered instead of relationships between the pixels. The results obtained could be observed in flow vector parameter and the segmentation colour based image for the time frame from 1 to 10 seconds. The visualization of both the parameters clarified the movement and changes of pixel intensity between two frames by the supportive colour segmentation, either in smooth or rough motion. Thus, this technique may contribute to others application such as biometrics, military system, and surveillance machine
Fusing Censored Dependent Data for Distributed Detection
In this paper, we consider a distributed detection problem for a censoring
sensor network where each sensor's communication rate is significantly reduced
by transmitting only "informative" observations to the Fusion Center (FC), and
censoring those deemed "uninformative". While the independence of data from
censoring sensors is often assumed in previous research, we explore spatial
dependence among observations. Our focus is on designing the fusion rule under
the Neyman-Pearson (NP) framework that takes into account the spatial
dependence among observations. Two transmission scenarios are considered, one
where uncensored observations are transmitted directly to the FC and second
where they are first quantized and then transmitted to further improve
transmission efficiency. Copula-based Generalized Likelihood Ratio Test (GLRT)
for censored data is proposed with both continuous and discrete messages
received at the FC corresponding to different transmission strategies. We
address the computational issues of the copula-based GLRTs involving
multidimensional integrals by presenting more efficient fusion rules, based on
the key idea of injecting controlled noise at the FC before fusion. Although,
the signal-to-noise ratio (SNR) is reduced by introducing controlled noise at
the receiver, simulation results demonstrate that the resulting noise-aided
fusion approach based on adding artificial noise performs very closely to the
exact copula-based GLRTs. Copula-based GLRTs and their noise-aided counterparts
by exploiting the spatial dependence greatly improve detection performance
compared with the fusion rule under independence assumption
Pilot-aided ML schemes for joint beamforming and channel estimation in WCDMA systems
This paper proposes semi-blind channel estimation and interference cancellation schemes for the reception of pilot-aided signals in WCDMA systems. It is shown that the performance of classical training-based schemes is severely degraded due to the code-multiplexing (as opposed to time-multiplexing) of traffic and pilot signals. The time-dispersivity of the channel destroys the orthogonality between the traffic signal and the pilot, and consequently the traffic signal appears as a new interfering signal for the channel estimation/beamforming algorithm. In order to avoid this effect, we propose techniques that exploit both the presence of the training sequence and the structure of the traffic signal in a semi-blind fashion.Peer ReviewedPostprint (published version
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