The Ricean K factor: Estimation and performance analysis

Cihan Tepedelenlioǧlu, Ali Abdi, Georgios B. Giannakis

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194 Scopus citations

Abstract

In wireless communications, the relative strength of the direct and scattered components of the received signal, as expressed by the Ricean K factor, provides an indication of link quality. Accordingly, efficient and accurate methods for estimating K are of considerable interest. In this paper, we propose a general class of moment-based estimators which use the signal envelope. This class of estimators unifies many of the previous estimators, and introduces new ones. We derive, for the first time, the asymptotic variance (AsV) of these estimators and compare them with the Cramér-Rao bound (CRB). We then tackle the problem of estimating K from the in-phase and quadrature-phase (I/Q) components of the received signal and illustrate the improvement in performance as compared with the envelope-based estimators. We derive the CRBs for the I/Q data model, which, unlike the envelope CRB, is tractable for correlated samples. Furthermore, we introduce a novel estimator that relies on the I/Q components, and derive its AsV even when the channel samples are correlated. We corroborate our analytical findings by simulations.

Original languageEnglish (US)
Pages (from-to)799-810
Number of pages12
JournalIEEE Transactions on Wireless Communications
Volume2
Issue number4
DOIs
StatePublished - Jul 2003

Bibliographical note

Funding Information:
Manuscript received December 26, 2001; revised August 23, 2002 and December 9, 2002; accepted December 9, 2002. The editor coordinating the review of this paper and approving it for publication is P. Driessen. The work of C. Te-pedelenliog˘lu was supported by the National Science Foundation through Career Grant CCR-0133841, and the work of G. B. Giannakis was supported by the Wireless Initiative Program under Grant 9970443 and by the NSF under Grant 01-05612. This paper was presented in part at the ICASSP, Orlando, FL, 2001, and in part at the Conference on Information Systems and Sciences, Princeton, NJ, March 2002.

Keywords

  • Detection and estimation
  • Propagation and channel characterization

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