Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Equitable Coloring Extends Chernoff-Hoeffding Bounds
APPROX '01/RANDOM '01 Proceedings of the 4th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems and 5th International Workshop on Randomization and Approximation Techniques in Computer Science: Approximation, Randomization and Combinatorial Optimization
Deterministic constructions of compressed sensing matrices
Journal of Complexity
Toeplitz-Structured Compressed Sensing Matrices
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
High-resolution radar via compressed sensing
IEEE Transactions on Signal Processing
Compressive Sensing by Random Convolution
SIAM Journal on Imaging Sciences
Identification of Matrices Having a Sparse Representation
IEEE Transactions on Signal Processing
Sparse Channel Estimation with Zero Tap Detection
IEEE Transactions on Wireless Communications
Data compression and harmonic analysis
IEEE Transactions on Information Theory
Decoding by linear programming
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Signal Reconstruction From Noisy Random Projections
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
A uniform uncertainty principle for Gaussian circulant matrices
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
SPARLS: the sparse RLS algorithm
IEEE Transactions on Signal Processing
Regularized recursive least squares for anomaly detection in sparse channel tracking applications
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
A Robust and Efficient Cross-Layer Optimal Design in Wireless Sensor Networks
Wireless Personal Communications: An International Journal
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Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm. In essence, CS enables the recovery of high-dimensional sparse signals from relatively few linear observations in the form of projections onto a collection of test vectors. Existing results show that if the entries of the test vectors are independent realizations of certain zero-mean random variables, then with high probability the unknown signals can be recovered by solving a tractable convex optimization. This work extends CS theory to settings where the entries of the test vectors exhibit structured statistical dependencies. It follows that CS can be effectively utilized in linear, time-invariant system identification problems provided the impulse response of the system is (approximately or exactly) sparse. An immediate application is in wireless multipath channel estimation. It is shown here that time-domain probing of a multipath channel with a random binary sequence, along with utilization of CS reconstruction techniques, can provide significant improvements in estimation accuracy compared to traditional least-squares based linear channel estimation strategies. Abstract extensions of the main results are also discussed, where the theory of equitable graph coloring is employed to establish the utility of CS in settings where the test vectors exhibit more general statistical dependencies.