Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Multiuser Detection
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Space Time Coding for Broadband Wireless Communications
Space Time Coding for Broadband Wireless Communications
A universal lattice code decoder for fading channels
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Sparsity-aware estimation of CDMA system parameters
EURASIP Journal on Advances in Signal Processing - Special issue on advanced equalization techniques for wireless communications
An alternating minimization method for sparse channel estimation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
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The number of active users in code-division multiple access (CDMA) systems is often much lower than the spreading gain. The present paper exploits fruitfully this a priori information to improve performance of multiuser detectors. A low-activity factor manifests itself in a sparse symbol vector with entries drawn from a finite alphabet that is augmented by the zero symbol to capture user inactivity. The nonequiprobable symbols of the augmented alphabet motivate a sparsity-exploiting maximum a posteriori probability (S-MAP) criterion, which is shown to yield a cost comprising the l2 least-squares error penalized by the p-th norm of the wanted symbol vector (p = 0, 1, 2). Related optimization problems appear in variable selection (shrinkage) schemes developed for linear regression, as well as in the emerging field of compressive sampling (CS). The contribution of this work to CDMA systems is a gamut of sparsity-embracing multiuser detectors trading off performance for complexity requirements. From the vantage point of CS and the least-absolute shrinkage selection operator (Lasso) spectrum of applications, the contribution amounts to sparsity-exploiting algorithms when the entries of the wanted signal vector adhere to finite-alphabet constraints.