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- Publisher Website: 10.1109/ASICON.2017.8252676
- Scopus: eid_2-s2.0-85044725140
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Conference Paper: Tensor-network-based predistorter design for multiple-input multiple-output nonlinear systems
Title | Tensor-network-based predistorter design for multiple-input multiple-output nonlinear systems |
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Authors | |
Issue Date | 2017 |
Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6805351 |
Citation | 2017 IEEE 12th International Conference on ASIC (ASICON), Guiyang, China, 25-28 October 2017, p. 1117-1120 How to Cite? |
Abstract | The recent development in tensor algorithms has shed new light on many design problems previously doomed by the curse of dimensionality. In particular, latest advances are seen in the tensor-network-based multiple-input multiple-output (MIMO) Volterra series modeling of nonlinear systems whereby the Volterra kernels can now be efficiently identified at an unprecedented order and memory length. Subsequent to nonlinear system identification, this paper studies the nonlinear MIMO predistorter design that is crucial for linearizing the response of nonlinear modules such as power amplifiers in mixed-signal applications. Two tensor-network-based predistorter design schemes are presented for the first time, whose effectiveness are validated through practical examples. |
Persistent Identifier | http://hdl.handle.net/10722/261961 |
DC Field | Value | Language |
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dc.contributor.author | Chen, C | - |
dc.contributor.author | Batselier, K | - |
dc.contributor.author | Telescu, M | - |
dc.contributor.author | Azou, S | - |
dc.contributor.author | Tanguy, N | - |
dc.contributor.author | Wong, N | - |
dc.date.accessioned | 2018-09-28T04:51:01Z | - |
dc.date.available | 2018-09-28T04:51:01Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | 2017 IEEE 12th International Conference on ASIC (ASICON), Guiyang, China, 25-28 October 2017, p. 1117-1120 | - |
dc.identifier.uri | http://hdl.handle.net/10722/261961 | - |
dc.description.abstract | The recent development in tensor algorithms has shed new light on many design problems previously doomed by the curse of dimensionality. In particular, latest advances are seen in the tensor-network-based multiple-input multiple-output (MIMO) Volterra series modeling of nonlinear systems whereby the Volterra kernels can now be efficiently identified at an unprecedented order and memory length. Subsequent to nonlinear system identification, this paper studies the nonlinear MIMO predistorter design that is crucial for linearizing the response of nonlinear modules such as power amplifiers in mixed-signal applications. Two tensor-network-based predistorter design schemes are presented for the first time, whose effectiveness are validated through practical examples. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6805351 | - |
dc.relation.ispartof | IEEE International Conference on ASIC Proceedings | - |
dc.rights | IEEE International Conference on ASIC Proceedings. Copyright © IEEE. | - |
dc.rights | ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.title | Tensor-network-based predistorter design for multiple-input multiple-output nonlinear systems | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Batselier, K: kbatseli@HKUCC-COM.hku.hk | - |
dc.identifier.email | Wong, N: nwong@eee.hku.hk | - |
dc.identifier.authority | Wong, N=rp00190 | - |
dc.identifier.doi | 10.1109/ASICON.2017.8252676 | - |
dc.identifier.scopus | eid_2-s2.0-85044725140 | - |
dc.identifier.hkuros | 292475 | - |
dc.identifier.spage | 1117 | - |
dc.identifier.epage | 1120 | - |
dc.publisher.place | United States | - |