Exploiting sparse user activity in multiuser detection

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Abstract

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 non-equiprobable 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 such sparse CDMA systems is a gamut of sparsity-exploiting 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.

Original languageEnglish (US)
Article number5671560
Pages (from-to)454-465
Number of pages12
JournalIEEE Transactions on Communications
Volume59
Issue number2
DOIs
StatePublished - Feb 2011

Bibliographical note

Funding Information:
This work was supported by the NSF grants CCF-0830480, 1016605, and ECCS-0824007, 1002180; and also through the collaborative participation in the Communications and Networks Consortium sponsored by the U. S. Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-2-0011. The U. S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. Part of this work was presented at the IEEE Intl. Symp. on Info. Theory, Seoul, South Korea, June 2009.

Keywords

  • Lasso
  • Sparsity
  • compressive sampling
  • multiuser detection
  • sphere decoding

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