The probability density function of SINR loss of the dominant mode rejection beamformer

Enlong Hu, Hongya Ge

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The dominant mode rejection (DMR) adaptive beamformer (ABF) is a reduced rank subspace algorithm, which replaces the covariance matrix in the Minimum Variance Distortionless Response (MVDR) beamformer with an eigenvalue reshaped covariance estimate out of the sample covariance matrix (SCM). Signal-to-interference-plus-noise ratio (SINR) loss quantifies the performance of an ABF designed with SCM with respect to its nominal version designed with the true ensemble covariance matrix (ECM). This paper conjectures that the SINR loss of the DMR beamformer is beta-distributed, derives and provides an approximated expression for the beta probability density function. For the simple case of single interferer present in white noise, the performance of the DMR beamformer only depends on the number of snapshots. Monte-Carlo simulations are carried out to verify our derived results.

Original languageEnglish (US)
Title of host publication2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538605790
DOIs
StatePublished - May 21 2018
Event52nd Annual Conference on Information Sciences and Systems, CISS 2018 - Princeton, United States
Duration: Mar 21 2018Mar 23 2018

Publication series

Name2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018

Other

Other52nd Annual Conference on Information Sciences and Systems, CISS 2018
CountryUnited States
CityPrinceton
Period3/21/183/23/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

Keywords

  • Minimum variance distortionless response (MVDR)
  • adaptive beamformer (ABF)
  • dominant mode rejection (DMR)
  • sample covariance matrix (SCM)

Fingerprint Dive into the research topics of 'The probability density function of SINR loss of the dominant mode rejection beamformer'. Together they form a unique fingerprint.

Cite this