arXiv subjects

Youngchul Sung

Publications and source records attributed to Youngchul Sung.

64 records · Page 4Linked to original sources

arXiv2011

A joint time-invariant filtering approach to the linear Gaussian relay problem

In this paper, the linear Gaussian relay problem is considered. Under the linear time-invariant (LTI) model the problem is formulated in the frequency domain based on the Toeplitz distribution theorem. Under the further assumption of realizable input spectra, the LTI Gaussian relay problem is converted to a joint design problem of source and relay filters under two power constraints, one at the source and the other at the relay, and a practical solution to this problem is proposed based on the projected subgradient method. Numerical results show that the proposed method yields a noticeable gain over the instantaneous amplify-and-forward (AF) scheme in inter-symbol interference (ISI) channels. Also, the optimality of the AF scheme within the class of one-tap relay filters is established in flat-fading channels.

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arXiv2010

On Beamformer Design for Multiuser MIMO Interference Channels

This paper considers several linear beamformer design paradigms for multiuser time-invariant multiple-input multiple-output interference channels. Notably, interference alignment and sum-rate based algorithms such as the maximum signal-to-interference-plus noise (max-SINR) algorithm are considered. Optimal linear beamforming under interference alignment consists of two layers; an inner precoder and decoder (or receive filter) accomplish interference alignment to eliminate inter-user interference, and an outer precoder and decoder diagonalize the effective single-user channel resulting from the interference alignment by the inner precoder and decoder. The relationship between this two-layer beamforming and the max-SINR algorithm is established at high signal-to-noise ratio. Also, the optimality of the max-SINR algorithm within the class of linear beamforming algorithms, and its local convergence with exponential rate, are established at high signal-to-noise ratio.

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arXiv2009

How Much Information can One Get from a Wireless Ad Hoc Sensor Network over a Correlated Random Field?

New large deviations results that characterize the asymptotic information rates for general $d$-dimensional ($d$-D) stationary Gaussian fields are obtained. By applying the general results to sensor nodes on a two-dimensional (2-D) lattice, the asymptotic behavior of ad hoc sensor networks deployed over correlated random fields for statistical inference is investigated. Under a 2-D hidden Gauss-Markov random field model with symmetric first order conditional autoregression and the assumption of no in-network data fusion, the behavior of the total obtainable information [nats] and energy efficiency [nats/J] defined as the ratio of total gathered information to the required energy is obtained as the coverage area, node density and energy vary. When the sensor node density is fixed, the energy efficiency decreases to zero with rate $Θ({area}^{-1/2})$ and the per-node information under fixed per-node energy also diminishes to zero with rate $O(N_t^{-1/3})$ as the number $N_t$ of network nodes increases by increasing the coverage area. As the sensor spacing $d_n$ increases, the per-node information converges to its limit $D$ with rate $D-\sqrt{d_n}e^{-αd_n}$ for a given diffusion rate $α$. When the coverage area is fixed and the node density increases, the per-node information is inversely proportional to the node density. As the total energy $E_t$ consumed in the network increases, the total information obtainable from the network is given by $O(\log E_t)$ for the fixed node density and fixed coverage case and by $Θ(E_t^{2/3})$ for the fixed per-node sensing energy and fixed density and increasing coverage case.

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arXiv2008

Optimal Node Density for Two-Dimensional Sensor Arrays

The problem of optimal node density for ad hoc sensor networks deployed for making inferences about two dimensional correlated random fields is considered. Using a symmetric first order conditional autoregressive Gauss-Markov random field model, large deviations results are used to characterize the asymptotic per-node information gained from the array. This result then allows an analysis of the node density that maximizes the information under an energy constraint, yielding insights into the trade-offs among the information, density and energy.

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arXiv2008

Information, Energy and Density for Ad Hoc Sensor Networks over Correlated Random Fields: Large Deviations Analysis

Using large deviations results that characterize the amount of information per node on a two-dimensional (2-D) lattice, asymptotic behavior of a sensor network deployed over a correlated random field for statistical inference is investigated. Under a 2-D hidden Gauss-Markov random field model with symmetric first order conditional autoregression, the behavior of the total information [nats] and energy efficiency [nats/J] defined as the ratio of total gathered information to the required energy is obtained as the coverage area, node density and energy vary.

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arXiv2008

Large Deviations Analysis for the Detection of 2D Hidden Gauss-Markov Random Fields Using Sensor Networks

The detection of hidden two-dimensional Gauss-Markov random fields using sensor networks is considered. Under a conditional autoregressive model, the error exponent for the Neyman-Pearson detector satisfying a fixed level constraint is obtained using the large deviations principle. For a symmetric first order autoregressive model, the error exponent is given explicitly in terms of the SNR and an edge dependence factor (field correlation). The behavior of the error exponent as a function of correlation strength is seen to divide into two regions depending on the value of the SNR. At high SNR, uncorrelated observations maximize the error exponent for a given SNR, whereas there is non-zero optimal correlation at low SNR. Based on the error exponent, the energy efficiency (defined as the ratio of the total information gathered to the total energy required) of ad hoc sensor network for detection is examined for two sensor deployment models: an infinite area model and and infinite density model. For a fixed sensor density, the energy efficiency diminishes to zero at rate O(area^{-1/2}) as the area is increased. On the other hand, non-zero efficiency is possible for increasing density depending on the behavior of the physical correlation as a function of the link length.

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arXiv2006

Neyman-Pearson Detection of Gauss-Markov Signals in Noise: Closed-Form Error Exponent and Properties

The performance of Neyman-Pearson detection of correlated stochastic signals using noisy observations is investigated via the error exponent for the miss probability with a fixed level. Using the state-space structure of the signal and observation model, a closed-form expression for the error exponent is derived, and the connection between the asymptotic behavior of the optimal detector and that of the Kalman filter is established. The properties of the error exponent are investigated for the scalar case. It is shown that the error exponent has distinct characteristics with respect to correlation strength: for signal-to-noise ratio (SNR) >1 the error exponent decreases monotonically as the correlation becomes stronger, whereas for SNR <1 there is an optimal correlation that maximizes the error exponent for a given SNR.

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arXiv2005

Optimal and Suboptimal Detection of Gaussian Signals in Noise: Asymptotic Relative Efficiency

The performance of Bayesian detection of Gaussian signals using noisy observations is investigated via the error exponent for the average error probability. Under unknown signal correlation structure or limited processing capability it is reasonable to use the simple quadratic detector that is optimal in the case of an independent and identically distributed (i.i.d.) signal. Using the large deviations principle, the performance of this detector (which is suboptimal for non-i.i.d. signals) is compared with that of the optimal detector for correlated signals via the asymptotic relative efficiency defined as the ratio between sample sizes of two detectors required for the same performance in the large-sample-size regime. The effects of SNR on the ARE are investigated. It is shown that the asymptotic efficiency of the simple quadratic detector relative to the optimal detector converges to one as the SNR increases without bound for any bounded spectrum, and that the simple quadratic detector performs as well as the optimal detector for a wide range of the correlation values at high SNR.

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arXiv2005

Sensor Configuration and Activation for Field Detection in Large Sensor Arrays

The problems of sensor configuration and activation for the detection of correlated random fields using large sensor arrays are considered. Using results that characterize the large-array performance of sensor networks in this application, the detection capabilities of different sensor configurations are analyzed and compared. The dependence of the optimal choice of configuration on parameters such as sensor signal-to-noise ratio (SNR), field correlation, etc., is examined, yielding insights into the most effective choices for sensor selection and activation in various operating regimes.

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arXiv2005

A Large Deviations Approach to Sensor Scheduling for Detection of Correlated Random Fields

The problem of scheduling sensor transmissions for the detection of correlated random fields using spatially deployed sensors is considered. Using the large deviations principle, a closed-form expression for the error exponent of the miss probability is given as a function of the sensor spacing and signal-to-noise ratio (SNR). It is shown that the error exponent has a distinct characteristic: at high SNR, the error exponent is monotonically increasing with respect to sensor spacing, while at low SNR there is an optimal spacing for scheduled sensors.

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