Low Complexity Sparse Bayesian Learning for Channel Estimation Using Generalized Mean Field

Niels Lovmand Pedersen, Carles Navarro Manchón, Bernard Henri Fleury

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

4 Citations (Scopus)
582 Downloads (Pure)

Abstract

We derive low complexity versions of a wide range of algorithms for sparse Bayesian learning (SBL) in underdetermined linear systems. The proposed algorithms are obtained by applying the generalized mean field (GMF) inference framework to a generic SBL probabilistic model. In the GMF framework, we constrain the auxiliary function approximating the posterior probability density function of the unknown variables to factorize over disjoint groups of contiguous entries in the sparse vector - the size of these groups dictates the degree of complexity reduction. The original high-complexity algorithms correspond to the particular case when all the entries of the sparse vector are assigned to one single group. Numerical investigations are conducted for both a generic compressive sensing application and for channel estimation in an orthogonal frequency-division multiplexing receiver. They show that, by choosing small group sizes, the resulting algorithms perform nearly as well as their original counterparts but with much less computational complexity.
Original languageEnglish
Title of host publicationEuropean Wireless 2014; 20th European Wireless Conference; Proceedings of
Number of pages6
PublisherIEEE Press
Publication date14 May 2014
Pages1-6
ISBN (Print)978-3-8007-3621-8
Publication statusPublished - 14 May 2014
EventThe 20th European Wireless (EW) Conference - Hotel Catalonia Plaza, Barcelona, Spain
Duration: 14 May 201416 May 2014

Conference

ConferenceThe 20th European Wireless (EW) Conference
LocationHotel Catalonia Plaza
Country/TerritorySpain
CityBarcelona
Period14/05/201416/05/2014
SeriesEuropean Wireless

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