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AuthorAmato, Nancy M. (28)Denny, Jory (12)Thomas, Shawna (10)Hassibi, Babak (6)Lee, Yi-Kuen (6)View MoreJournal2014 IEEE International Conference on Robotics and Automation (ICRA) (7)2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (6)2012 11th International Symposium on Parallel and Distributed Computing (5)2010 35th IEEE Photovoltaic Specialists Conference (3)2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science (3)View MoreKAUST Grant NumberKUS-C1-016-04 (37)025478 (7)UK-C0020 (5)UK-c0020 (5)SA-C0040 (4)View MorePublisher

Institute of Electrical and Electronics Engineers (IEEE) (131)

SubjectCUDA (4)GPU (4)sparse grids (3)Electroporation (2)Graph signal processing (2)View MoreType
Conference Paper (131)

Year (Issue Date)2017 (4)2016 (6)2015 (9)2014 (13)2013 (28)View MoreItem AvailabilityMetadata Only (131)

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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.

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.

Balanced Reed-Solomon codes for all parameters

Halbawi, Wael; Liu, Zihan; Hassibi, Babak (2016 IEEE Information Theory Workshop (ITW), Institute of Electrical and Electronics Engineers (IEEE), 2016-10-27) [Conference Paper]

We construct balanced and sparsest generator matrices for cyclic Reed-Solomon codes with any length n and dimension k. By sparsest, we mean that each row has the least possible number of nonzeros, while balanced means that the number of nonzeros in any two columns differs by at most one. Codes allowing such encoding schemes are useful in distributed settings where computational load-balancing is critical. The problem was first studied by Dau et al. who showed, using probabilistic arguments, that there always exists an MDS code over a sufficiently large field such that its generator matrix is both sparsest and balanced. Motivated by the need for an explicit construction with efficient decoding, the authors of the current paper showed that the generator matrix of a cyclic Reed-Solomon code of length n and dimension k can always be transformed to one that is both sparsest and balanced, when n and k are such that k/n (n-k+1) is an integer. In this paper, we lift this condition and construct balanced and sparsest generator matrices for cyclic Reed-Solomon codes for any set of parameters.

Subsampling for graph power spectrum estimation

Chepuri, Sundeep Prabhakar; Leus, Geert (2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Institute of Electrical and Electronics Engineers (IEEE), 2016-10-06) [Conference Paper]

In this paper we focus on subsampling stationary random signals that reside on the vertices of undirected graphs. Second-order stationary graph signals are obtained by filtering white noise and they admit a well-defined power spectrum. Estimating the graph power spectrum forms a central component of stationary graph signal processing and related inference tasks. We show that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the power spectrum of the graph signal from the subsampled observations, without any spectral priors. In addition, a near-optimal greedy algorithm is developed to design the subsampling scheme.

(Almost) practical tree codes

Khina, Anatoly; Halbawi, Wael; Hassibi, Babak (2016 IEEE International Symposium on Information Theory (ISIT), Institute of Electrical and Electronics Engineers (IEEE), 2016-08-15) [Conference Paper]

We consider the problem of stabilizing an unstable plant driven by bounded noise over a digital noisy communication link, a scenario at the heart of networked control. To stabilize such a plant, one needs real-time encoding and decoding with an error probability profile that decays exponentially with the decoding delay. The works of Schulman and Sahai over the past two decades have developed the notions of tree codes and anytime capacity, and provided the theoretical framework for studying such problems. Nonetheless, there has been little practical progress in this area due to the absence of explicit constructions of tree codes with efficient encoding and decoding algorithms. Recently, linear time-invariant tree codes were proposed to achieve the desired result under maximum-likelihood decoding. In this work, we take one more step towards practicality, by showing that these codes can be efficiently decoded using sequential decoding algorithms, up to some loss in performance (and with some practical complexity caveats). We supplement our theoretical results with numerical simulations that demonstrate the effectiveness of the decoder in a control system setting.

Efficient optimal joint channel estimation and data detection for massive MIMO systems

Alshamary, Haider Ali Jasim; Xu, Weiyu (2016 IEEE International Symposium on Information Theory (ISIT), Institute of Electrical and Electronics Engineers (IEEE), 2016-08-15) [Conference Paper]

In this paper, we propose an efficient optimal joint channel estimation and data detection algorithm for massive MIMO wireless systems. Our algorithm is optimal in terms of the generalized likelihood ratio test (GLRT). For massive MIMO systems, we show that the expected complexity of our algorithm grows polynomially in the channel coherence time. Simulation results demonstrate significant performance gains of our algorithm compared with suboptimal non-coherent detection algorithms. To the best of our knowledge, this is the first algorithm which efficiently achieves GLRT-optimal non-coherent detections for massive MIMO systems with general constellations.

Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals

Cho, Myung; Cai, Jian-Feng; Liu, Suhui; Eldar, Yonina C.; Xu, Weiyu (2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers (IEEE), 2016-06-24) [Conference Paper]

We propose fast algorithms that speed up or improve the performance of recovering spectrally sparse signals from un-derdetermined measurements. Our algorithms are based on a non-convex approach of using alternating projected gradient descent for structured matrix recovery. We apply this approach to two formulations of structured matrix recovery: Hankel and Toeplitz mosaic structured matrix, and Hankel structured matrix. Our methods provide better recovery performance, and faster signal recovery than existing algorithms, including atomic norm minimization.

Ber analysis of the box relaxation for BPSK signal recovery

Thrampoulidis, Christos; Abbasi, Ehsan; Xu, Weiyu; Hassibi, Babak (2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers (IEEE), 2016-06-24) [Conference Paper]

We study the problem of recovering an n-dimensional BPSK signal from m linear noise-corrupted measurements using the box relaxation method which relaxes the discrete set {±1}n to the convex set [-1,1]n to obtain a convex optimization algorithm followed by hard thresholding. When the noise and measurement matrix have iid standard normal entries, we obtain an exact expression for the bit-wise probability of error Pe in the limit of n and m growing and m/n fixed. At high SNR our result shows that the Pe of box relaxation is within 3dB of the matched filter bound (MFB) for square systems, and that it approaches the (MFB) as m grows large compared to n. Our results also indicate that as m, n → ∞, for any fixed set of size k, the error events of the corresponding k bits in the box relaxation method are independent.

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