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Digital Self-interference Cancellation Algorithm Based on The Movable Shape Filter

Published: 25 May 2020 Publication History

Abstract

In digital self-inference cancellation field, the adaptive filter is the main way to eliminate self-interference. A mass of research is completed to optimize the performance of the adaptive algorithm. Nevertheless, we have noticed there are few scholars to modify the shape of the filter to achieve a better cancellation effect. Constrained by the fixed and immovable shape, the typical adaptive filter can't full use the information behind the digital signal. Stimulated by this problem, a lot of work have been fulfilled in order to acquire a more flexible filter shape. In this paper, we propose a novel filter architecture named as the movable shape filter. Through adding the position parameters to the typical filter, finer elimination results have been reached. Moreover, the rigorous simulation results show a great progress in the mean square error.

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ICVISP 2019: Proceedings of the 3rd International Conference on Vision, Image and Signal Processing
August 2019
584 pages
ISBN:9781450376259
DOI:10.1145/3387168
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 May 2020

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Author Tags

  1. Adaptive Filter Structure
  2. Digital Self-inference Cancellation
  3. Movable Shape Filter

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  • Research-article
  • Research
  • Refereed limited

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ICVISP 2019

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ICVISP 2019 Paper Acceptance Rate 126 of 277 submissions, 45%;
Overall Acceptance Rate 186 of 424 submissions, 44%

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