Stochastic blind equalization based on PDF fitting using Parzen estimator
This work presents a new blind equalization approach that aims to force the probability density function (pdf) at the equalizer output to match the known constellation pdf. Quadratic distance between pdf's is used as the cost function to be minimized. The proposed method relies on the Parzen wi...
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| Veröffentlicht in: | IEEE transactions on signal processing 2005-02, Vol.53 (2), p.696-704 |
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| container_title | IEEE transactions on signal processing |
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| creator | Lazaro, M. Santamaria, I. Erdogmus, D. Hild, K.E. Pantaleon, C. Principe, J.C. |
| description | This work presents a new blind equalization approach that aims to force the probability density function (pdf) at the equalizer output to match the known constellation pdf. Quadratic distance between pdf's is used as the cost function to be minimized. The proposed method relies on the Parzen window method to estimate the data pdf and is implemented by a stochastic gradient descent algorithm. The kernel size of the Parzen estimator allows a dual mode switch or a soft switch between blind and decision-directed equalization. The proposed method converges faster than the constant modulus algorithm (CMA) working at the symbol rate, with a similar computational burden, and reduces the residual error of the CMA in multilevel modulations at the same time. A comparison with the most common blind techniques is presented. |
| doi_str_mv | 10.1109/TSP.2004.840767 |
| format | Article |
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Quadratic distance between pdf's is used as the cost function to be minimized. The proposed method relies on the Parzen window method to estimate the data pdf and is implemented by a stochastic gradient descent algorithm. The kernel size of the Parzen estimator allows a dual mode switch or a soft switch between blind and decision-directed equalization. The proposed method converges faster than the constant modulus algorithm (CMA) working at the symbol rate, with a similar computational burden, and reduces the residual error of the CMA in multilevel modulations at the same time. A comparison with the most common blind techniques is presented.</description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/TSP.2004.840767</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithms ; Applied sciences ; Blind equalization ; Blind equalizers ; Blinds ; CMA ; Convergence ; Cost function ; Detection, estimation, filtering, equalization, prediction ; Equalization ; Estimators ; Exact sciences and technology ; Higher order statistics ; Impedance matching ; information theory ; Information, signal and communications theory ; Intersymbol interference ; Nonlinear filters ; Parzen windowing ; PDF ; Probability density function ; Probability density functions ; Signal and communications theory ; Signal, noise ; Stochastic processes ; Stochasticity ; Switches ; Telecommunications and information theory</subject><ispartof>IEEE transactions on signal processing, 2005-02, Vol.53 (2), p.696-704</ispartof><rights>2005 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Quadratic distance between pdf's is used as the cost function to be minimized. The proposed method relies on the Parzen window method to estimate the data pdf and is implemented by a stochastic gradient descent algorithm. The kernel size of the Parzen estimator allows a dual mode switch or a soft switch between blind and decision-directed equalization. The proposed method converges faster than the constant modulus algorithm (CMA) working at the symbol rate, with a similar computational burden, and reduces the residual error of the CMA in multilevel modulations at the same time. A comparison with the most common blind techniques is presented.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Blind equalization</subject><subject>Blind equalizers</subject><subject>Blinds</subject><subject>CMA</subject><subject>Convergence</subject><subject>Cost function</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Equalization</subject><subject>Estimators</subject><subject>Exact sciences and technology</subject><subject>Higher order statistics</subject><subject>Impedance matching</subject><subject>information theory</subject><subject>Information, signal and communications theory</subject><subject>Intersymbol interference</subject><subject>Nonlinear filters</subject><subject>Parzen windowing</subject><subject>PDF</subject><subject>Probability density function</subject><subject>Probability density functions</subject><subject>Signal and communications theory</subject><subject>Signal, noise</subject><subject>Stochastic processes</subject><subject>Stochasticity</subject><subject>Switches</subject><subject>Telecommunications and information theory</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kc1LwzAYxoMoOKdnD16KoHjpljSfPcp0Ohg42ARvIU1TzejaLWkP7q83tYOBByEkL7y_50nePABcIzhCCKbj1XIxSiAkI0EgZ_wEDFBKUAwJZ6ehhhTHVPCPc3Dh_RpCREjKBmC2bGr9pXxjdZSVtsojs2tVafeqsXUVZcqbPArF4mkaFbZpbPUZtb7bF8rtTRWZIN2opnaX4KxQpTdXh3MI3qfPq8lrPH97mU0e57HGImliI1hemIQnTPEU5ZQInGGoKTSEhR6FGhVacZ5liudUIYopTbDBKUVYi4ThIbjvfbeu3rXhermxXpuyVJWpWy8TAdPwHx348C-IGEeE8uTX8_YPuq5bV4UxpGApIt1zAzTuIe1q750p5NaF0d23RFB2EcgQgewikH0EQXF3sFVeq7JwqtLWH2WMdIsE7qbnrDHm2MYCcQbxDwGrjUY</recordid><startdate>20050201</startdate><enddate>20050201</enddate><creator>Lazaro, M.</creator><creator>Santamaria, I.</creator><creator>Erdogmus, D.</creator><creator>Hild, K.E.</creator><creator>Pantaleon, C.</creator><creator>Principe, J.C.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Quadratic distance between pdf's is used as the cost function to be minimized. The proposed method relies on the Parzen window method to estimate the data pdf and is implemented by a stochastic gradient descent algorithm. The kernel size of the Parzen estimator allows a dual mode switch or a soft switch between blind and decision-directed equalization. The proposed method converges faster than the constant modulus algorithm (CMA) working at the symbol rate, with a similar computational burden, and reduces the residual error of the CMA in multilevel modulations at the same time. A comparison with the most common blind techniques is presented.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TSP.2004.840767</doi><tpages>9</tpages></addata></record> |
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| ispartof | IEEE transactions on signal processing, 2005-02, Vol.53 (2), p.696-704 |
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| subjects | Algorithms Applied sciences Blind equalization Blind equalizers Blinds CMA Convergence Cost function Detection, estimation, filtering, equalization, prediction Equalization Estimators Exact sciences and technology Higher order statistics Impedance matching information theory Information, signal and communications theory Intersymbol interference Nonlinear filters Parzen windowing Probability density function Probability density functions Signal and communications theory Signal, noise Stochastic processes Stochasticity Switches Telecommunications and information theory |
| title | Stochastic blind equalization based on PDF fitting using Parzen estimator |
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