Adaptive Signal Detection in Subspace Interference with Uncertain Prior Knowledge

Yuan Jiang, Hongbin Li, Muralidhar Rangaswamy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a new Bayesian learning algorithm, referred to as the mSKL-GAMP, for signal detection in subspace interference with uncertain/partial prior knowledge of the subspace. It is an extension of the recently introduced SKL algorithm [1] that employs a fixed dictionary for subspace recovery, which causes the grid-mismatch problem. mSKL-GAMP overcomes the problem via a subspace refining procedure. In addition, it integrates the generalized approximate message passing (GAMP) for posterior approximation, which bypasses iterative matrix inversions required by SKL, and thus is computationally much simpler. Numerical results show mSKL-GAMP yields improved detection performance over SKL and other benchmark schemes.

Original languageEnglish
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
Pages74-78
Number of pages5
ISBN (Electronic)9781728143002
DOIs
StatePublished - Nov 2019
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: 3 Nov 20196 Nov 2019

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2019-November
ISSN (Print)1058-6393

Conference

Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Country/TerritoryUnited States
CityPacific Grove
Period3/11/196/11/19

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