Channel estimation using implicit training
In this paper, a new method to perform channel estimation is presented. It is shown that accurate estimation can be obtained when a training sequence is actually arithmetically added to the information data as opposed to being placed in a separate empty time slot: hence, the word "implicit.&quo...
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| Veröffentlicht in: | IEEE transactions on signal processing 2004-01, Vol.52 (1), p.240-254 |
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| container_title | IEEE transactions on signal processing |
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| creator | Orozco-Lugo, A.G. Lara, M.M. McLernon, D.C. |
| description | In this paper, a new method to perform channel estimation is presented. It is shown that accurate estimation can be obtained when a training sequence is actually arithmetically added to the information data as opposed to being placed in a separate empty time slot: hence, the word "implicit." A closed-form solution for the estimation variance is derived, as well as the Cramer-Rao lower bound. Conditions are derived for the training sequences that result in a channel estimation performance that is independent of the channel characteristics. In addition, estimation performance is shown to be independent of the modulation format. A procedure to synthesize optimal training sequences is presented, and the problem of synchronization is solved. The performance of the algorithm is then compared with other methods that use explicit training under GSM-like environmental conditions, and the new algorithm is shown to be competitive with these. Finally, comparisons are also carried out against blind methods over realistic bandlimited channels, and these show that the new method exhibits good performance. |
| doi_str_mv | 10.1109/TSP.2003.819993 |
| format | Article |
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It is shown that accurate estimation can be obtained when a training sequence is actually arithmetically added to the information data as opposed to being placed in a separate empty time slot: hence, the word "implicit." A closed-form solution for the estimation variance is derived, as well as the Cramer-Rao lower bound. Conditions are derived for the training sequences that result in a channel estimation performance that is independent of the channel characteristics. In addition, estimation performance is shown to be independent of the modulation format. A procedure to synthesize optimal training sequences is presented, and the problem of synchronization is solved. The performance of the algorithm is then compared with other methods that use explicit training under GSM-like environmental conditions, and the new algorithm is shown to be competitive with these. Finally, comparisons are also carried out against blind methods over realistic bandlimited channels, and these show that the new method exhibits good performance.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Bandwidth</subject><subject>Channel estimation</subject><subject>Channels</subject><subject>Cities and towns</subject><subject>Closed-form solution</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Exact sciences and technology</subject><subject>Exact solutions</subject><subject>Higher order statistics</subject><subject>Information systems</subject><subject>Information, signal and communications theory</subject><subject>Mathematical analysis</subject><subject>Modulation</subject><subject>Optimization</subject><subject>Receiving antennas</subject><subject>Signal and communications theory</subject><subject>Signal, noise</subject><subject>Studies</subject><subject>Synchronization</subject><subject>Telecommunications and information theory</subject><subject>Training</subject><subject>Transmitters</subject><subject>Transmitting antennas</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kE1LAzEQhhdRsFbPHrwsgiLCtpNNNh9HKX5BQcEK3kI2TTRlm63J7sF_b8oWCh48hAmZZ14mT5adI5ggBGK6eHudlAB4wpEQAh9kIyQIKoAwepjuUOGi4uzjODuJcQWACBF0lN3OvpT3pslN7Nxada71eR-d_8zdetM47bq8C8r59HKaHVnVRHO2q-Ps_eF-MXsq5i-Pz7O7eaExh67QlJKKEWTU0lq8VBxro4QhQKyomMGMM7A1CEsM5jVSrCKKsprWChHKAeNxdj3kbkL73ae95NpFbZpGedP2UZa8BI4IJPDmXxBRlrB0tpmXf9BV2wefviE5xzT5EyRB0wHSoY0xGCs3ITkJPxKB3DqWybHcOpaD4zRxtYtVUavGBuW1i_uxCgvGKU3cxcA5Y8y-XVbJCuBfeGaCrA</recordid><startdate>200401</startdate><enddate>200401</enddate><creator>Orozco-Lugo, A.G.</creator><creator>Lara, M.M.</creator><creator>McLernon, D.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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It is shown that accurate estimation can be obtained when a training sequence is actually arithmetically added to the information data as opposed to being placed in a separate empty time slot: hence, the word "implicit." A closed-form solution for the estimation variance is derived, as well as the Cramer-Rao lower bound. Conditions are derived for the training sequences that result in a channel estimation performance that is independent of the channel characteristics. In addition, estimation performance is shown to be independent of the modulation format. A procedure to synthesize optimal training sequences is presented, and the problem of synchronization is solved. The performance of the algorithm is then compared with other methods that use explicit training under GSM-like environmental conditions, and the new algorithm is shown to be competitive with these. 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| subjects | Algorithms Applied sciences Bandwidth Channel estimation Channels Cities and towns Closed-form solution Detection, estimation, filtering, equalization, prediction Exact sciences and technology Exact solutions Higher order statistics Information systems Information, signal and communications theory Mathematical analysis Modulation Optimization Receiving antennas Signal and communications theory Signal, noise Studies Synchronization Telecommunications and information theory Training Transmitters Transmitting antennas |
| title | Channel estimation using implicit training |
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