Monte Carlo Methods for Channel, Phase Noise, and Frequency Offset Estimation With Unknown Noise Variances in OFDM Systems
In this paper, we address the problem of orthogonal frequency-division multiplexing (OFDM) channel estimation in the presence of phase noise (PHN) and carrier frequency offset (CFO). In OFDM systems, PHN and CFO cause two effects: the common phase error (CPE) and the intercarrier interference (ICI)...
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| Veröffentlicht in: | IEEE transactions on signal processing 2008-08, Vol.56 (8), p.3613-3626 |
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| creator | Septier, F. Delignon, Y. Menhaj-Rivenq, A. Garnier, C. |
| description | In this paper, we address the problem of orthogonal frequency-division multiplexing (OFDM) channel estimation in the presence of phase noise (PHN) and carrier frequency offset (CFO). In OFDM systems, PHN and CFO cause two effects: the common phase error (CPE) and the intercarrier interference (ICI) which severely degrade the accuracy of the channel estimate. In literature, several algorithms have been proposed to solve this problem. Nevertheless, in all these existing schemes, both the PHN and the additive white Gaussian noise (AWGN) powers are assumed to be known. Because no a priori knowledge of PHN and AWGN powers is available at the receiver, we propose different strategies for the estimation of channel impulse response (CIR), CFO, PHN, and also the PHN and the AWGN powers. Based on Monte Carlo methods, the proposed approaches estimate these many unknowns in the time domain from a training OFDM symbol using either offline or online estimators. In the online case, we propose sequential Monte Carlo algorithms and especially an original maximization step of the joint a posteriori probability density function for the unknown parameters. Simulation results are provided to illustrate the efficiency of the proposed algorithms in terms of mean square error (MSE) on channel, phase distortions, and also noise power estimation. |
| doi_str_mv | 10.1109/TSP.2008.919629 |
| format | Article |
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In OFDM systems, PHN and CFO cause two effects: the common phase error (CPE) and the intercarrier interference (ICI) which severely degrade the accuracy of the channel estimate. In literature, several algorithms have been proposed to solve this problem. Nevertheless, in all these existing schemes, both the PHN and the additive white Gaussian noise (AWGN) powers are assumed to be known. Because no a priori knowledge of PHN and AWGN powers is available at the receiver, we propose different strategies for the estimation of channel impulse response (CIR), CFO, PHN, and also the PHN and the AWGN powers. Based on Monte Carlo methods, the proposed approaches estimate these many unknowns in the time domain from a training OFDM symbol using either offline or online estimators. In the online case, we propose sequential Monte Carlo algorithms and especially an original maximization step of the joint a posteriori probability density function for the unknown parameters. Simulation results are provided to illustrate the efficiency of the proposed algorithms in terms of mean square error (MSE) on channel, phase distortions, and also noise power estimation.</description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/TSP.2008.919629</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Additive white noise ; Algorithms ; Applied sciences ; AWGN ; Carrier frequency offset (CFO) ; Channel estimation ; Channels ; Computer Science ; Computer simulation ; Detection, estimation, filtering, equalization, prediction ; Engineering Sciences ; Estimates ; Exact sciences and technology ; Frequency division multiplexing ; Frequency estimation ; Gaussian noise ; Impulse response ; Information, signal and communications theory ; Interference ; Mean square errors ; Miscellaneous ; Monte Carlo methods ; Multiplexing ; Noise ; OFDM ; On-line systems ; online parameter estimation ; optimal importance function ; Orthogonal Frequency Division Multiplexing ; orthogonal frequency-division multiplexing (OFDM) ; Phase estimation ; Phase noise ; Rao-Blackwellization ; sequential Monte Carlo (SMC) methods ; Signal and communications theory ; Signal and Image processing ; Signal processing ; Signal, noise ; Studies ; Telecommunications and information theory</subject><ispartof>IEEE transactions on signal processing, 2008-08, Vol.56 (8), p.3613-3626</ispartof><rights>2008 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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In OFDM systems, PHN and CFO cause two effects: the common phase error (CPE) and the intercarrier interference (ICI) which severely degrade the accuracy of the channel estimate. In literature, several algorithms have been proposed to solve this problem. Nevertheless, in all these existing schemes, both the PHN and the additive white Gaussian noise (AWGN) powers are assumed to be known. Because no a priori knowledge of PHN and AWGN powers is available at the receiver, we propose different strategies for the estimation of channel impulse response (CIR), CFO, PHN, and also the PHN and the AWGN powers. Based on Monte Carlo methods, the proposed approaches estimate these many unknowns in the time domain from a training OFDM symbol using either offline or online estimators. In the online case, we propose sequential Monte Carlo algorithms and especially an original maximization step of the joint a posteriori probability density function for the unknown parameters. Simulation results are provided to illustrate the efficiency of the proposed algorithms in terms of mean square error (MSE) on channel, phase distortions, and also noise power estimation.</description><subject>Additive white noise</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>AWGN</subject><subject>Carrier frequency offset (CFO)</subject><subject>Channel estimation</subject><subject>Channels</subject><subject>Computer Science</subject><subject>Computer simulation</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Engineering Sciences</subject><subject>Estimates</subject><subject>Exact sciences and technology</subject><subject>Frequency division multiplexing</subject><subject>Frequency estimation</subject><subject>Gaussian noise</subject><subject>Impulse response</subject><subject>Information, signal and communications theory</subject><subject>Interference</subject><subject>Mean square errors</subject><subject>Miscellaneous</subject><subject>Monte Carlo methods</subject><subject>Multiplexing</subject><subject>Noise</subject><subject>OFDM</subject><subject>On-line systems</subject><subject>online parameter estimation</subject><subject>optimal importance function</subject><subject>Orthogonal Frequency Division Multiplexing</subject><subject>orthogonal frequency-division multiplexing (OFDM)</subject><subject>Phase estimation</subject><subject>Phase noise</subject><subject>Rao-Blackwellization</subject><subject>sequential Monte Carlo (SMC) methods</subject><subject>Signal and communications theory</subject><subject>Signal and Image processing</subject><subject>Signal processing</subject><subject>Signal, noise</subject><subject>Studies</subject><subject>Telecommunications and information theory</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9ks1vEzEQxVcIJErgzIGLhUQBqUn9tf44VmlDkRJSqS1ws5zdsXbLxi72pij89Xi1VQ4cerLl-c3TvPErircEzwjB-vTm-mpGMVYzTbSg-llxRDQnU8yleJ7vuGTTUsmfL4tXKd1hTDjX4qj4uwq-BzS3sQtoBX0T6oRciGjeWO-hO0FXjU2AvoU2wQmyvkaLCL934Ks9WjuXoEcXqW-3tm-DRz_avkG3_pcPf_zYg77b2FpfQUKtR-vF-Qpd71MP2_S6eOFsl-DN4zkpbhcXN_PL6XL95ev8bDmtuNL9lJAK9IbXm4pAXVLMsdoIrkoGUBNRl8pq50pcOwwMnK0kcZRKRgRjILWs2aT4POo2tjP3MY8a9ybY1lyeLc3wlpdGGJX0gWT248jex5BNpt5s21RB11kPYZeMUlhwISXN5PGTJONKCUZ0Bj89CRIhCeVimHdSvP8PvQu76PNyTNaiQuly0DsdoSqGlCK4gyeCzZAEk5NghiSYMQm548OjrE2V7VzM39GmQxvFJS2pFpl7N3ItABzKvBQyO2b_AIV6ugk</recordid><startdate>20080801</startdate><enddate>20080801</enddate><creator>Septier, F.</creator><creator>Delignon, Y.</creator><creator>Menhaj-Rivenq, A.</creator><creator>Garnier, C.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-6145-8475</orcidid><orcidid>https://orcid.org/0000-0001-9392-674X</orcidid><orcidid>https://orcid.org/0000-0001-5931-6091</orcidid></search><sort><creationdate>20080801</creationdate><title>Monte Carlo Methods for Channel, Phase Noise, and Frequency Offset Estimation With Unknown Noise Variances in OFDM Systems</title><author>Septier, F. ; Delignon, Y. ; Menhaj-Rivenq, A. ; Garnier, C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c489t-11ce9b4dbc1ed520408b64853eed16d58a9ff50df0e3efac71f22731633e797d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Additive white noise</topic><topic>Algorithms</topic><topic>Applied sciences</topic><topic>AWGN</topic><topic>Carrier frequency offset (CFO)</topic><topic>Channel estimation</topic><topic>Channels</topic><topic>Computer Science</topic><topic>Computer simulation</topic><topic>Detection, estimation, filtering, equalization, prediction</topic><topic>Engineering Sciences</topic><topic>Estimates</topic><topic>Exact sciences and technology</topic><topic>Frequency division multiplexing</topic><topic>Frequency estimation</topic><topic>Gaussian noise</topic><topic>Impulse response</topic><topic>Information, signal and communications theory</topic><topic>Interference</topic><topic>Mean square errors</topic><topic>Miscellaneous</topic><topic>Monte Carlo methods</topic><topic>Multiplexing</topic><topic>Noise</topic><topic>OFDM</topic><topic>On-line systems</topic><topic>online parameter estimation</topic><topic>optimal importance function</topic><topic>Orthogonal Frequency Division Multiplexing</topic><topic>orthogonal frequency-division multiplexing (OFDM)</topic><topic>Phase estimation</topic><topic>Phase noise</topic><topic>Rao-Blackwellization</topic><topic>sequential Monte Carlo (SMC) methods</topic><topic>Signal and communications theory</topic><topic>Signal and Image processing</topic><topic>Signal processing</topic><topic>Signal, noise</topic><topic>Studies</topic><topic>Telecommunications and information theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Septier, F.</creatorcontrib><creatorcontrib>Delignon, Y.</creatorcontrib><creatorcontrib>Menhaj-Rivenq, A.</creatorcontrib><creatorcontrib>Garnier, C.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>IEEE transactions on signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Septier, F.</au><au>Delignon, Y.</au><au>Menhaj-Rivenq, A.</au><au>Garnier, C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Monte Carlo Methods for Channel, Phase Noise, and Frequency Offset Estimation With Unknown Noise Variances in OFDM Systems</atitle><jtitle>IEEE transactions on signal processing</jtitle><stitle>TSP</stitle><date>2008-08-01</date><risdate>2008</risdate><volume>56</volume><issue>8</issue><spage>3613</spage><epage>3626</epage><pages>3613-3626</pages><issn>1053-587X</issn><eissn>1941-0476</eissn><coden>ITPRED</coden><abstract>In this paper, we address the problem of orthogonal frequency-division multiplexing (OFDM) channel estimation in the presence of phase noise (PHN) and carrier frequency offset (CFO). In OFDM systems, PHN and CFO cause two effects: the common phase error (CPE) and the intercarrier interference (ICI) which severely degrade the accuracy of the channel estimate. In literature, several algorithms have been proposed to solve this problem. Nevertheless, in all these existing schemes, both the PHN and the additive white Gaussian noise (AWGN) powers are assumed to be known. Because no a priori knowledge of PHN and AWGN powers is available at the receiver, we propose different strategies for the estimation of channel impulse response (CIR), CFO, PHN, and also the PHN and the AWGN powers. Based on Monte Carlo methods, the proposed approaches estimate these many unknowns in the time domain from a training OFDM symbol using either offline or online estimators. In the online case, we propose sequential Monte Carlo algorithms and especially an original maximization step of the joint a posteriori probability density function for the unknown parameters. Simulation results are provided to illustrate the efficiency of the proposed algorithms in terms of mean square error (MSE) on channel, phase distortions, and also noise power estimation.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TSP.2008.919629</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-6145-8475</orcidid><orcidid>https://orcid.org/0000-0001-9392-674X</orcidid><orcidid>https://orcid.org/0000-0001-5931-6091</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Additive white noise Algorithms Applied sciences AWGN Carrier frequency offset (CFO) Channel estimation Channels Computer Science Computer simulation Detection, estimation, filtering, equalization, prediction Engineering Sciences Estimates Exact sciences and technology Frequency division multiplexing Frequency estimation Gaussian noise Impulse response Information, signal and communications theory Interference Mean square errors Miscellaneous Monte Carlo methods Multiplexing Noise OFDM On-line systems online parameter estimation optimal importance function Orthogonal Frequency Division Multiplexing orthogonal frequency-division multiplexing (OFDM) Phase estimation Phase noise Rao-Blackwellization sequential Monte Carlo (SMC) methods Signal and communications theory Signal and Image processing Signal processing Signal, noise Studies Telecommunications and information theory |
| title | Monte Carlo Methods for Channel, Phase Noise, and Frequency Offset Estimation With Unknown Noise Variances in OFDM Systems |
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