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AuthorHassibi, Babak (5)Kostina, Victoria (2)Alanwar, Amr (1)Anwar, Fatima M. (1)Cai, Jian-Feng (1)View MoreJournal2016 IEEE 55th Conference on Decision and Control (CDC) (2)2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2)55th AIAA Aerospace Sciences Meeting (2)2016 50th Asilomar Conference on Signals, Systems and Computers (1)2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton) (1)View MoreKAUST Grant NumberKAUST-002 (1)OCRF-2014-CRG-3 (1)OSR-2015-Sensors-2700 (1)PublisherInstitute of Electrical and Electronics Engineers (IEEE) (10)American Institute of Aeronautics and Astronautics (AIAA) (2)Society of Exploration Geophysicists (1)Subject4G (1)Approximation algorithms (1)Blind equalizers (1)Compressed sensing (1)Convolution (1)View MoreType

Conference Paper (13)

Year (Issue Date)
2017 (13)

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Location-aware network operation for cloud radio access network

Wang, Fanggang; Ruan, Liangzhong; Win, Moe Z. (2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers (IEEE), 2017-06-20) [Conference Paper]

One of the major challenges in effectively operating a cloud radio access network (C-RAN) is the excessive overhead signaling and computation load that scale rapidly with the size of the network. In this paper, the exploitation of location information of the mobile devices is proposed to address this challenge. We consider an approach in which location-assisted channel state information (CSI) acquisition methods are introduced to complement conventional pilot-based CSI acquisition methods and avoid excessive overhead signaling. A low-complexity algorithm is designed to maximize the sum rate. An adaptive algorithm is also proposed to address the uncertainty issue in CSI acquisition. Both theoretical and numerical analyses show that location information provides a new dimension to improve throughput for next-generation massive cooperative networks.

Large scale 2D spectral compressed sensing in continuous domain

Cai, Jian-Feng; Xu, Weiyu; Yang, Yang (2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers (IEEE), 2017-06-20) [Conference Paper]

We consider the problem of spectral compressed sensing in continuous domain, which aims to recover a 2-dimensional spectrally sparse signal from partially observed time samples. The signal is assumed to be a superposition of s complex sinusoids. We propose a semidefinite program for the 2D signal recovery problem. Our model is able to handle large scale 2D signals of size 500 × 500, whereas traditional approaches only handle signals of size around 20 × 20.

Constrained blind deconvolution using Wirtinger flow methods

Walk, Philipp; Jung, Peter; Hassibi, Babak (2017 International Conference on Sampling Theory and Applications (SampTA), Institute of Electrical and Electronics Engineers (IEEE), 2017-09-04) [Conference Paper]

In this work we consider one-dimensional blind deconvolution with prior knowledge of signal autocorrelations in the classical framework of polynomial factorization. In particular this univariate case highly suffers from several non-trivial ambiguities and therefore blind deconvolution is known to be ill-posed in general. However, if additional autocorrelation information is available and the corresponding polynomials are co-prime, blind deconvolution is uniquely solvable up to global phase. Using lifting, the outer product of the unknown vectors is the solution to a (convex) semi-definite program (SDP) demonstrating that -theoretically- recovery is computationally tractable. However, for practical applications efficient algorithms are required which should operate in the original signal space. To this end we also discuss a gradient descent algorithm (Wirtinger flow) for the original non-convex problem. We demonstrate numerically that such an approach has performance comparable to the semidefinite program in the noisy case. Our work is motivated by applications in blind communication scenarios and we will discuss a specific signaling scheme where information is encoded into polynomial roots.

Improved bounds on the epidemic threshold of exact SIS models on complex networks

Ruhi, Navid Azizan; Thrampoulidis, Christos; Hassibi, Babak (2016 IEEE 55th Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2017-01-05) [Conference Paper]

The SIS (susceptible-infected-susceptible) epidemic model on an arbitrary network, without making approximations, is a 2n-state Markov chain with a unique absorbing state (the all-healthy state). This makes analysis of the SIS model and, in particular, determining the threshold of epidemic spread quite challenging. It has been shown that the exact marginal probabilities of infection can be upper bounded by an n-dimensional linear time-invariant system, a consequence of which is that the Markov chain is “fast-mixing” when the LTI system is stable, i.e. when equation (where β is the infection rate per link, δ is the recovery rate, and λmax(A) is the largest eigenvalue of the network's adjacency matrix). This well-known threshold has been recently shown not to be tight in several cases, such as in a star network. In this paper, we provide tighter upper bounds on the exact marginal probabilities of infection, by also taking pairwise infection probabilities into account. Based on this improved bound, we derive tighter eigenvalue conditions that guarantee fast mixing (i.e., logarithmic mixing time) of the chain. We demonstrate the improvement of the threshold condition by comparing the new bound with the known one on various networks with various epidemic parameters.

Subgraph detection using graph signals

Chepuri, Sundeep Prabhakar; Leus, Geert (2016 50th Asilomar Conference on Signals, Systems and Computers, Institute of Electrical and Electronics Engineers (IEEE), 2017-03-06) [Conference Paper]

In this paper we develop statistical detection theory for graph signals. In particular, given two graphs, namely, a background graph that represents an usual activity and an alternative graph that represents some unusual activity, we are interested in answering the following question: To which of the two graphs does the observed graph signal fit the best? To begin with, we assume both the graphs are known, and derive an optimal Neyman-Pearson detector. Next, we derive a suboptimal detector for the case when the alternative graph is not known. The developed theory is illustrated with numerical experiments.

Computational Studies of Positive and Negative Streamers in Bubbles Suspended in Distilled Water

Sharma, Ashish; Levko, Dmitry; Raja, Laxminarayan L. (55th AIAA Aerospace Sciences Meeting, American Institute of Aeronautics and Astronautics (AIAA), 2017-01-05) [Conference Paper]

We perform computational studies of nanosecond streamers generated in helium bubbles immersed in distilled water under high pressure conditions. The model takes into account the presence of water vapor in the gas bubble for an accurate description of the chemical kinetics of the discharge. We apply positive and negative trigger voltages much higher than the breakdown voltage and study the dynamic characteristics of the resulting discharge. We observe that, for high positive trigger voltages, the streamer moves along the surface of the gas bubble during the initial stages of the discharge. We also find a considerable difference in the evolution of the streamer discharge for positive and negative trigger voltages with more uniform volumetric distribution of species in the streamer channel for negative trigger voltages due to formation of multiple streamers. We also observe that the presence of water vapor does not influence the breakdown voltage of the discharge but greatly affects the composition of dominant species in the trail of the streamer channel.

Multi-rate control over AWGN channels via analog joint source-channel coding

Khina, Anatoly; Pettersson, Gustav M.; Kostina, Victoria; Hassibi, Babak (2016 IEEE 55th Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2017-01-05) [Conference Paper]

We consider the problem of controlling an unstable plant over an additive white Gaussian noise (AWGN) channel with a transmit power constraint, where the signaling rate of communication is larger than the sampling rate (for generating observations and applying control inputs) of the underlying plant. Such a situation is quite common since sampling is done at a rate that captures the dynamics of the plant and which is often much lower than the rate that can be communicated. This setting offers the opportunity of improving the system performance by employing multiple channel uses to convey a single message (output plant observation or control input). Common ways of doing so are through either repeating the message, or by quantizing it to a number of bits and then transmitting a channel coded version of the bits whose length is commensurate with the number of channel uses per sampled message. We argue that such “separated source and channel coding” can be suboptimal and propose to perform joint source-channel coding. Since the block length is short we obviate the need to go to the digital domain altogether and instead consider analog joint source-channel coding. For the case where the communication signaling rate is twice the sampling rate, we employ the Archimedean bi-spiral-based Shannon-Kotel'nikov analog maps to show significant improvement in stability margins and linear-quadratic Gaussian (LQG) costs over simple schemes that employ repetition.

On the Role of Chemical Kinetics Modeling in the LES of Premixed Bluff Body and Backward-Facing Step Combustors

Chakroun, Nadim W.; Shanbhogue, Santosh J.; Kewlani, Gaurav; Taamallah, Soufien; Michaels, Dan; Ghoniem, Ahmed (55th AIAA Aerospace Sciences Meeting, American Institute of Aeronautics and Astronautics (AIAA), 2017-01-05) [Conference Paper]

Recirculating flows in the wake of a bluff body, behind a sudden expansion or down-stream of a swirler, are pivotal for anchoring a flame and expanding the stability range. The size and structure of these recirculation zones and the accurate prediction of the length of these zones is a very important characteristic that computational simulations should have. Large eddy simulation (LES) techniques with an appropriate combustion model and reaction mechanism afford a balance between computational complexity and predictive accuracy. In this study, propane/air mixtures were simulated in a bluff-body stabilized combustor based on the Volvo test case and also in a backward-facing step combustor.
The main goal is to investigate the role of the chemical mechanism and the accuracy of estimating the extinction strain rate on the prediction of important ow features such as recirculation zones. Two 2-step mechanisms were employed, one which gave reasonable extinction strain rates and another modi ed 2-step mechanism where it grossly over-predicted the values. This modified mechanism under-predicted recirculation zone lengths compared to the original mechanism and had worse agreement with experiments in both geometries. While the recirculation zone lengths predicted by both reduced mechanisms in the step combustor scale linearly with the extinction strain rate, the scaling curves do not match experimental results as none of the simpli ed mechanisms produce extinction strain rates that are consistent with those predicted by the comprehensive mechanisms. We conclude that it is very important that a chemical mechanism is able to correctly predict extinction strain rates if it is to be used in CFD simulations.

Rate-cost tradeoffs in control

Kostina, Victoria; Hassibi, Babak (2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Institute of Electrical and Electronics Engineers (IEEE), 2017-02-13) [Conference Paper]

Consider a distributed control problem with a communication channel connecting the observer of a linear stochastic system to the controller. The goal of the controller is minimize a quadratic cost function. The most basic special case of that cost function is the mean-square deviation of the system state from the desired state. We study the fundamental tradeoff between the communication rate r bits/sec and the limsup of the expected cost b, and show a lower bound on the rate necessary to attain b. The bound applies as long as the system noise has a probability density function. If target cost b is not too large, that bound can be closely approached by a simple lattice quantization scheme that only quantizes the innovation, that is, the difference between the controller's belief about the current state and the true state.

Born reflection kernel analysis and wave-equation reflection traveltime inversion in elastic media

Wang, Tengfei; Cheng, Jiubing (SEG Technical Program Expanded Abstracts 2017, Society of Exploration Geophysicists, 2017-08-17) [Conference Paper]

Elastic reflection waveform inversion (ERWI) utilize the reflections to update the low and intermediate wavenumbers in the deeper part of model. However, ERWI suffers from the cycle-skipping problem due to the objective function of waveform residual. Since traveltime information relates to the background model more linearly, we use the traveltime residuals as objective function to update background velocity model using wave equation reflected traveltime inversion (WERTI). The reflection kernel analysis shows that mode decomposition can suppress the artifacts in gradient calculation. We design a two-step inversion strategy, in which PP reflections are firstly used to invert P wave velocity (Vp), followed by S wave velocity (Vs) inversion with PS reflections. P/S separation of multi-component seismograms and spatial wave mode decomposition can reduce the nonlinearity of inversion effectively by selecting suitable P or S wave subsets for hierarchical inversion. Numerical example of Sigsbee2A model validates the effectiveness of the algorithms and strategies for elastic WERTI (E-WERTI).

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