OFDM receiver for fast time-varying channels using block-sparse Bayesian learning

Oana-Elena Barbu, Carles Navarro Manchón, Christian Rom, Tommaso Balercia, Bernard Henri Fleury

Research output: Contribution to journalLetterResearchpeer-review

8 Citations (Scopus)

Abstract

We propose an iterative algorithm for OFDM receivers operating over fast time-varying channels. The design relies on the assumptions that the channel response can be characterized by a few non-negligible separable multipath components, and the temporal variation of each component gain can be well characterized with a basis expansion model using a small number of terms. As a result, the channel estimation problem is posed as that of estimating a vector of complex coefficients that exhibits a block-sparse structure, which we solve with tools from block-sparse Bayesian learning. Using variational Bayesian inference, we embed the channel estimator in a receiver structure that performs iterative channel and noise precision estimation, intercarrier interference cancellation, detection and decoding. Simulation results illustrate the superior performance of the proposed receiver over state-of-art receivers.
Original languageEnglish
JournalI E E E Transactions on Vehicular Technology
Volume65
Issue number12
Pages (from-to)10053-10057
ISSN0018-9545
DOIs
Publication statusPublished - Dec 2016

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Channel estimation
Orthogonal frequency division multiplexing
Decoding

Cite this

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title = "OFDM receiver for fast time-varying channels using block-sparse Bayesian learning",
abstract = "We propose an iterative algorithm for OFDM receivers operating over fast time-varying channels. The design relies on the assumptions that the channel response can be characterized by a few non-negligible separable multipath components, and the temporal variation of each component gain can be well characterized with a basis expansion model using a small number of terms. As a result, the channel estimation problem is posed as that of estimating a vector of complex coefficients that exhibits a block-sparse structure, which we solve with tools from block-sparse Bayesian learning. Using variational Bayesian inference, we embed the channel estimator in a receiver structure that performs iterative channel and noise precision estimation, intercarrier interference cancellation, detection and decoding. Simulation results illustrate the superior performance of the proposed receiver over state-of-art receivers.",
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OFDM receiver for fast time-varying channels using block-sparse Bayesian learning. / Barbu, Oana-Elena; Manchón, Carles Navarro; Rom, Christian ; Balercia, Tommaso; Fleury, Bernard Henri.

In: I E E E Transactions on Vehicular Technology, Vol. 65, No. 12, 12.2016, p. 10053-10057.

Research output: Contribution to journalLetterResearchpeer-review

TY - JOUR

T1 - OFDM receiver for fast time-varying channels using block-sparse Bayesian learning

AU - Barbu, Oana-Elena

AU - Manchón, Carles Navarro

AU - Rom, Christian

AU - Balercia, Tommaso

AU - Fleury, Bernard Henri

PY - 2016/12

Y1 - 2016/12

N2 - We propose an iterative algorithm for OFDM receivers operating over fast time-varying channels. The design relies on the assumptions that the channel response can be characterized by a few non-negligible separable multipath components, and the temporal variation of each component gain can be well characterized with a basis expansion model using a small number of terms. As a result, the channel estimation problem is posed as that of estimating a vector of complex coefficients that exhibits a block-sparse structure, which we solve with tools from block-sparse Bayesian learning. Using variational Bayesian inference, we embed the channel estimator in a receiver structure that performs iterative channel and noise precision estimation, intercarrier interference cancellation, detection and decoding. Simulation results illustrate the superior performance of the proposed receiver over state-of-art receivers.

AB - We propose an iterative algorithm for OFDM receivers operating over fast time-varying channels. The design relies on the assumptions that the channel response can be characterized by a few non-negligible separable multipath components, and the temporal variation of each component gain can be well characterized with a basis expansion model using a small number of terms. As a result, the channel estimation problem is posed as that of estimating a vector of complex coefficients that exhibits a block-sparse structure, which we solve with tools from block-sparse Bayesian learning. Using variational Bayesian inference, we embed the channel estimator in a receiver structure that performs iterative channel and noise precision estimation, intercarrier interference cancellation, detection and decoding. Simulation results illustrate the superior performance of the proposed receiver over state-of-art receivers.

U2 - 10.1109/TVT.2016.2554611

DO - 10.1109/TVT.2016.2554611

M3 - Letter

VL - 65

SP - 10053

EP - 10057

JO - I E E E Transactions on Vehicular Technology

JF - I E E E Transactions on Vehicular Technology

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ER -