Reservoir Computing-Based Digital Self-Interference Cancellation for In-Band Full-Duplex Radios
Digital self-interference cancellation (DSIC) has become a pivotal strategy for implementing in-band full-duplex (IBFD) radios to overcome the hurdles posed by residual self-interference that persist after propagation and analog domain cancellation. This work proposes a novel reservoir computing-bas...
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| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
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| Online Access: | https://ieeexplore.ieee.org/document/10556632/ |
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| author | Zhikai Liu Haifeng Luo Tharmalingam Ratnarajah |
| author_facet | Zhikai Liu Haifeng Luo Tharmalingam Ratnarajah |
| author_sort | Zhikai Liu |
| collection | DOAJ |
| description | Digital self-interference cancellation (DSIC) has become a pivotal strategy for implementing in-band full-duplex (IBFD) radios to overcome the hurdles posed by residual self-interference that persist after propagation and analog domain cancellation. This work proposes a novel reservoir computing-based DSIC (RC-DSIC) technique and compares it with traditional polynomial-based (PL-DSIC) and various existing neural network-based (NN-DSIC) approaches. We begin by delineating the structure of the RC and exploring its capability to address the DSIC task, highlighting its potential advantages over current methodologies. Subsequently, we examine the computational complexity of these approaches and undertake extensive simulations to compare the proposed RC-DSIC approach against PL-DSIC and existing NN-DSIC schemes. Our results reveal that the RC-DSIC scheme attains 99.84% of the performance offered by PL-based DSIC algorithms while requiring only 1.51% of the computational demand. Compared to many existing NN-DSIC schemes, the RC-DSIC method achieves at least 99.73% of its performance with no more than 36.61% of the computational demand. This performance justifies the viability of RC-DSIC as an effective and efficient solution for DSIC in IBFD, striking it is a better implementation method in terms of computational simplicity. |
| format | Article |
| id | doaj-art-ec39c0bd5b2f4c7d9b22a9abfa3cd2e5 |
| institution | DOAJ |
| issn | 2831-316X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-ec39c0bd5b2f4c7d9b22a9abfa3cd2e52025-08-20T02:53:09ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2024-01-01285586810.1109/TMLCN.2024.341429610556632Reservoir Computing-Based Digital Self-Interference Cancellation for In-Band Full-Duplex RadiosZhikai Liu0https://orcid.org/0000-0002-2438-4470Haifeng Luo1https://orcid.org/0000-0003-0943-5716Tharmalingam Ratnarajah2https://orcid.org/0000-0002-7636-1246Institute for Imaging, Data and Communications (IDCOM), The University of Edinburgh, Edinburgh, U.K.Institute for Imaging, Data and Communications (IDCOM), The University of Edinburgh, Edinburgh, U.K.Institute for Imaging, Data and Communications (IDCOM), The University of Edinburgh, Edinburgh, U.K.Digital self-interference cancellation (DSIC) has become a pivotal strategy for implementing in-band full-duplex (IBFD) radios to overcome the hurdles posed by residual self-interference that persist after propagation and analog domain cancellation. This work proposes a novel reservoir computing-based DSIC (RC-DSIC) technique and compares it with traditional polynomial-based (PL-DSIC) and various existing neural network-based (NN-DSIC) approaches. We begin by delineating the structure of the RC and exploring its capability to address the DSIC task, highlighting its potential advantages over current methodologies. Subsequently, we examine the computational complexity of these approaches and undertake extensive simulations to compare the proposed RC-DSIC approach against PL-DSIC and existing NN-DSIC schemes. Our results reveal that the RC-DSIC scheme attains 99.84% of the performance offered by PL-based DSIC algorithms while requiring only 1.51% of the computational demand. Compared to many existing NN-DSIC schemes, the RC-DSIC method achieves at least 99.73% of its performance with no more than 36.61% of the computational demand. This performance justifies the viability of RC-DSIC as an effective and efficient solution for DSIC in IBFD, striking it is a better implementation method in terms of computational simplicity.https://ieeexplore.ieee.org/document/10556632/Digital self-interference cancellationin-band full-duplexreservoir computing |
| spellingShingle | Zhikai Liu Haifeng Luo Tharmalingam Ratnarajah Reservoir Computing-Based Digital Self-Interference Cancellation for In-Band Full-Duplex Radios IEEE Transactions on Machine Learning in Communications and Networking Digital self-interference cancellation in-band full-duplex reservoir computing |
| title | Reservoir Computing-Based Digital Self-Interference Cancellation for In-Band Full-Duplex Radios |
| title_full | Reservoir Computing-Based Digital Self-Interference Cancellation for In-Band Full-Duplex Radios |
| title_fullStr | Reservoir Computing-Based Digital Self-Interference Cancellation for In-Band Full-Duplex Radios |
| title_full_unstemmed | Reservoir Computing-Based Digital Self-Interference Cancellation for In-Band Full-Duplex Radios |
| title_short | Reservoir Computing-Based Digital Self-Interference Cancellation for In-Band Full-Duplex Radios |
| title_sort | reservoir computing based digital self interference cancellation for in band full duplex radios |
| topic | Digital self-interference cancellation in-band full-duplex reservoir computing |
| url | https://ieeexplore.ieee.org/document/10556632/ |
| work_keys_str_mv | AT zhikailiu reservoircomputingbaseddigitalselfinterferencecancellationforinbandfullduplexradios AT haifengluo reservoircomputingbaseddigitalselfinterferencecancellationforinbandfullduplexradios AT tharmalingamratnarajah reservoircomputingbaseddigitalselfinterferencecancellationforinbandfullduplexradios |