Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding
Journal of Multivariate Analysis
Sparsity-embracing multiuser detection for CDMA systems with low activity factor
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
A sparsity detection framework for on-off random access channels
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
IEEE Transactions on Signal Processing
Distributed spectrum sensing for cognitive radio networks by exploiting sparsity
IEEE Transactions on Signal Processing
An efficient code-timing estimator for DS-CDMA signals
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind decorrelating RAKE receivers for long-code WCDMA
IEEE Transactions on Signal Processing
On parameter estimation in long-code DS/CDMA systems: Cramer-Rao bounds and least-squares algorithms
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Fast Solution of -Norm Minimization Problems When the Solution May Be Sparse
IEEE Transactions on Information Theory
Spreading codes for direct sequence CDMA and wideband CDMA cellular networks
IEEE Communications Magazine
Wideband DS-CDMA for next-generation mobile communications systems
IEEE Communications Magazine
The software radio architecture
IEEE Communications Magazine
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The number of active users, their timing offsets, and their (possibly dispersive) channels with the access point are performance-critical parameters for wireless code division multiple access (CDMA). Estimating them as accurately as possible using as short as possible training sequences can markedly improve error performance as well as the capacity of CDMA systems. The fresh look advocated here permeates benefits from recent advances in variable selection and compressive sampling approaches to multiuser communications by casting estimation of these parameters as a sparse linear regression problem. Novel estimators are developed by exploiting two forms of sparsity present: the first emerging from user (in)activity, and the second from the uncertainty on user delays and channel taps. Simulated tests demonstrate a large gain in performance when sparsity-aware estimators of CDMA parameters are compared to sparsity-agnostic standard least-squares-based alternatives.