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102
Support Recovery for the Drift Coefficient of HighDimensional Diffusions
"... AbstractConsider the problem of learning the drift coefficient of a pdimensional stochastic differential equation from a sample path of length T . We assume that the drift is parametrized by a highdimensional vector, and study the support recovery problem when both p and T can tend to infinity. ..."
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describes which degrees of freedom interact under the dynamics. In this case, we analyze a 1regularized least squares estimator and prove an upper bound on T that nearly matches the lower bound on specific classes of sparse matrices.
1Support Recovery for the Drift Coefficient of HighDimensional Diffusions
"... Abstract—Consider the problem of learning the drift coefficient of a pdimensional stochastic differential equation from a sample path of length T. We assume that the drift is parametrized by a highdimensional vector, and study the support recovery problem when both p and T can tend to infinity. In ..."
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describes which degrees of freedom interact under the dynamics. In this case, we analyze a `1regularized least squares estimator and prove an upper bound on T that nearly matches the lower bound on specific classes of sparse matrices. Index Terms—Stochastic differential equation, sparse recovery, dynamical
Nonadaptive Group Testing: Explicit bounds and novel algorithms
, 2012
"... We present computationally efficient and provably correct algorithms with nearoptimal samplecomplexity for noisy nonadaptive group testing. Group testing involves grouping arbitrary subsets of items into pools. Each pool is then tested to identify the defective items, which are usually assumed to ..."
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Cited by 17 (4 self)
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algorithms for group testing, namely, Combinatorial Orthogonal Matching Pursuit (COMP), Combinatorial Basis Pursuit (CBP), and CBP via Linear Programming (CBPLP) decoding. The first and third of these algorithms have several flavours, dealing separately with the noiseless and noisy measurement scenarios. We
1Performance Bounds for Grouped Incoherent Measurements in Compressive Sensing
"... Compressive sensing (CS) allows for acquisition of sparse signals at sampling rates significantly lower than the Nyquist rate required for bandlimited signals. Recovery guarantees for CS are generally derived based on the assumption that measurement projections are selected independently at random. ..."
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Compressive sensing (CS) allows for acquisition of sparse signals at sampling rates significantly lower than the Nyquist rate required for bandlimited signals. Recovery guarantees for CS are generally derived based on the assumption that measurement projections are selected independently at random
1ReducedDimension Multiuser Detection
"... We present a reduceddimension multiuser detector (RDMUD) structure that significantly decreases the number of required correlation branches at the receiver frontend, while still achieving performance similar to that of the conventional matchedfilter (MF) bank. RDMUD exploits the fact that the n ..."
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to determine active users and sign detection for data recovery, and the reduceddimension decisionfeedback (RDDF) detector, which combines decisionfeedback orthogonal matching pursuit for active user detection and sign detection for data recovery. We identify conditions such that error is dominated by active
Onoff random access channels: A compressed sensing framework
, 2009
"... This paper considers a simple on–off random multiple access channel, where n users communicate simultaneously to a single receiver over m degrees of freedom. Each user transmits with probability λ, where typically λn < m ≪ n, and the receiver must detect which users transmitted. We show that whe ..."
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Cited by 31 (6 self)
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that when the codebook has i.i.d. Gaussian entries, detecting which users transmitted is mathematically equivalent to a certain sparsity detection problem considered in compressed sensing. Using recent sparsity results, we derive upper and lower bounds on the capacities of these channels. We show
Minimum Variance Estimation of a Sparse Vector Within the Linear Gaussian Model: An
"... Abstract — We consider minimum variance estimation within the sparse linear Gaussian model (SLGM). A sparse vector is to be estimated from a linearly transformed version embedded in Gaussian noise. Our analysis is based on the theory of reproducing kernel Hilbert spaces (RKHS). After a characterizat ..."
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characterization of the RKHS associated with the SLGM, we derive a lower bound on the minimum variance achievable by estimators with a prescribed bias function, including the important special case of unbiased estimation. This bound is obtained via an orthogonal projection of the prescribed mean function onto a
1Spatial Compressive Sensing for MIMO Radar
"... Abstract—We study compressive sensing in the spatial domain to achieve target localization, specifically direction of arrival (DOA), using multipleinput multipleoutput (MIMO) radar. A sparse localization framework is proposed for a MIMO array in which transmit and receive elements are placed at ra ..."
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at random. This allows for a dramatic reduction in the number of elements needed, while still attaining performance comparable to that of a filled (Nyquist) array. By leveraging properties of structured random matrices, we develop a bound on the coherence of the resulting measurement matrix, and obtain
1Compressive Demodulation of Mutually Interfering Signals
"... MultiUser Detection is fundamental not only to cellular wireless communication but also to RadioFrequency Identification (RFID) technology that supports supply chain management. The challenge of Multiuser Detection (MUD) is that of demodulating mutually interfering signals, and the two biggest im ..."
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measurements. This paper begins by unifying two frontend architectures proposed for MUD by showing that both lead to the same discrete signal model. Algorithms are presented for coherent and noncoherent detection that are based on iterative matching pursuit. Noncoherent detection is all that is needed
Results 1  10
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102