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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)PublisherIEEE (6)Institute of Electrical and Electronics Engineers (IEEE) (4)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)

Item AvailabilityMetadata Only (13)

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Cyclops: PRU programming framework for precise timing applications

Alanwar, Amr; Anwar, Fatima M.; Zhang, Yi-Fan; Pearson, Justin; Hespanha, Joao; Srivastava, Mani B. (2017 IEEE International Symposium on Precision Clock Synchronization for Measurement, Control, and Communication (ISPCS), IEEE, 2017-10-05) [Conference Paper]

The Beaglebone Black single-board computer is well-suited for real-time embedded applications because its system-on-a-chip contains two

Constrained blind deconvolution using Wirtinger flow methods

Walk, Philipp; Jung, Peter; Hassibi, Babak (2017 International Conference on Sampling Theory and Applications (SampTA), 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.

Balanced and sparse Tamo-Barg codes

Halbawi, Wael; Duursma, Iwan; Dau, Hoang; Hassibi, Babak (2017 IEEE International Symposium on Information Theory (ISIT), IEEE, 2017-08-29) [Conference Paper]

We construct balanced and sparse generator matrices for Tamo and Barg's Locally Recoverable Codes (LRCs). More specifically, for a cyclic Tamo-Barg code of length n, dimension k and locality r, we show how to deterministically construct a generator matrix where the number of nonzeros in any two columns differs by at most one, and where the weight of every row is d + r - 1, where d is the minimum distance of the code. Since LRCs are designed mainly for distributed storage systems, the results presented in this work provide a computationally balanced and efficient encoding scheme for these codes. The balanced property ensures that the computational effort exerted by any storage node is essentially the same, whilst the sparse property ensures that this effort is minimal. The work presented in this paper extends a similar result previously established for Reed-Solomon (RS) codes, where it is now known that any cyclic RS code possesses a generator matrix that is balanced as described, but is sparsest, meaning that each row has d nonzeros.

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).

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), 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), 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.

Low profile frequency agile MIMO slot antenna with TCM characterization

Ghalib, Asim; Hussain, Rifaqat; Sharawi, Mohammad S. (2017 11th European Conference on Antennas and Propagation (EUCAP), IEEE, 2017-06-07) [Conference Paper]

In this paper, a frequency reconfigurable multiple-input-multiple-output (MIMO) slot antenna is presented. The proposed design is low profile and compact with wide tunability range, covering several well-known frequency bands from 1800 MHz to 2450 MHz. The frequency reconfigurability is achieved by loading the annular slot with varactor diodes. The antenna system is also analyzed for MIMO performance metrics. Moreover, the effect of circular slot antenna on the chassis modes is also investigated using the theory of characteristic modes (TCM). The physical principle behind frequency reconfigurability is also investigated using TCM analysis. An interesting finding is observed using varactor diodes for frequency reconfigurability, that is the reactive impedance loading does not alter the modal significance (MS) plots but only aid in the input impedance matching at different frequency bands.

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.

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.

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.

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