Digital Self-Interference Cancellation With Robust Multi-layered Total Least Mean Squares Adaptive Filters Article Swipe
Shiyu Song
,
Yanqun Tang
,
Xizhang Wei
,
Yu Zhou
,
Xianjie Lu
,
Zhengpeng Wang
,
Songhu Ge
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.03137
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.03137
In simultaneous transmit and receive (STAR) wireless communications, digital self-interference (SI) cancellation is required before estimating the remote transmission (RT) channel. Considering the inherent connection between SI channel reconstruction and RT channel estimation, we propose a multi-layered M-estimate total least mean squares (m-MTLS) joint estimator to estimate both channels. In each layer, our proposed m-MTLS estimator first employs an M-estimate total least mean squares (MTLS) algorithm to eliminate residual SI from the received signal and give a new estimation of the RT channel. Then, it gives the final RT channel estimation based on the weighted sum of the estimation values obtained from each layer. Compared to traditional minimum mean square error (MMSE) estimator and single-layered MTLS estimator, it demonstrates that the m-MTLS estimator has better performance of normalized mean squared difference (NMSD). Besides, the simulation results also show the robustness of m-MTLS estimator even in scenarios where the local reference signal is contaminated with noise, and the received signal is impacted by strong impulse noise.
Related Topics
Concepts
Estimator
Minimum mean square error
Mean squared error
Robustness (evolution)
Least mean squares filter
Algorithm
Impulse noise
Mathematics
Least-squares function approximation
Channel (broadcasting)
Recursive least squares filter
Statistics
Computer science
Control theory (sociology)
Adaptive filter
Telecommunications
Artificial intelligence
Biochemistry
Gene
Control (management)
Chemistry
Pixel
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2308.03137
- https://arxiv.org/pdf/2308.03137
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385681560
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385681560Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2308.03137Digital Object Identifier
- Title
-
Digital Self-Interference Cancellation With Robust Multi-layered Total Least Mean Squares Adaptive FiltersWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-06Full publication date if available
- Authors
-
Shiyu Song, Yanqun Tang, Xizhang Wei, Yu Zhou, Xianjie Lu, Zhengpeng Wang, Songhu GeList of authors in order
- Landing page
-
https://arxiv.org/abs/2308.03137Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2308.03137Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2308.03137Direct OA link when available
- Concepts
-
Estimator, Minimum mean square error, Mean squared error, Robustness (evolution), Least mean squares filter, Algorithm, Impulse noise, Mathematics, Least-squares function approximation, Channel (broadcasting), Recursive least squares filter, Statistics, Computer science, Control theory (sociology), Adaptive filter, Telecommunications, Artificial intelligence, Biochemistry, Gene, Control (management), Chemistry, PixelTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 16, 22, 71, 80, 86, 93, 97, 120, 133, 138, 147, 156 |
| abstract_inverted_index.(RT) | 19 |
| abstract_inverted_index.(SI) | 10 |
| abstract_inverted_index.MTLS | 115 |
| abstract_inverted_index.also | 136 |
| abstract_inverted_index.both | 47 |
| abstract_inverted_index.each | 50, 102 |
| abstract_inverted_index.even | 143 |
| abstract_inverted_index.from | 70, 101 |
| abstract_inverted_index.give | 75 |
| abstract_inverted_index.mean | 40, 62, 108, 128 |
| abstract_inverted_index.show | 137 |
| abstract_inverted_index.that | 119 |
| abstract_inverted_index.with | 153 |
| abstract_inverted_index.Then, | 83 |
| abstract_inverted_index.based | 91 |
| abstract_inverted_index.error | 110 |
| abstract_inverted_index.final | 87 |
| abstract_inverted_index.first | 56 |
| abstract_inverted_index.gives | 85 |
| abstract_inverted_index.joint | 43 |
| abstract_inverted_index.least | 39, 61 |
| abstract_inverted_index.local | 148 |
| abstract_inverted_index.total | 38, 60 |
| abstract_inverted_index.where | 146 |
| abstract_inverted_index.(MMSE) | 111 |
| abstract_inverted_index.(MTLS) | 64 |
| abstract_inverted_index.(STAR) | 5 |
| abstract_inverted_index.before | 14 |
| abstract_inverted_index.better | 124 |
| abstract_inverted_index.layer, | 51 |
| abstract_inverted_index.layer. | 103 |
| abstract_inverted_index.m-MTLS | 54, 121, 141 |
| abstract_inverted_index.noise, | 154 |
| abstract_inverted_index.noise. | 164 |
| abstract_inverted_index.remote | 17 |
| abstract_inverted_index.signal | 73, 150, 158 |
| abstract_inverted_index.square | 109 |
| abstract_inverted_index.strong | 162 |
| abstract_inverted_index.values | 99 |
| abstract_inverted_index.(NMSD). | 131 |
| abstract_inverted_index.between | 25 |
| abstract_inverted_index.channel | 27, 31, 89 |
| abstract_inverted_index.digital | 8 |
| abstract_inverted_index.employs | 57 |
| abstract_inverted_index.impulse | 163 |
| abstract_inverted_index.minimum | 107 |
| abstract_inverted_index.propose | 34 |
| abstract_inverted_index.receive | 4 |
| abstract_inverted_index.results | 135 |
| abstract_inverted_index.squared | 129 |
| abstract_inverted_index.squares | 41, 63 |
| abstract_inverted_index.(m-MTLS) | 42 |
| abstract_inverted_index.Besides, | 132 |
| abstract_inverted_index.Compared | 104 |
| abstract_inverted_index.channel. | 20, 82 |
| abstract_inverted_index.estimate | 46 |
| abstract_inverted_index.impacted | 160 |
| abstract_inverted_index.inherent | 23 |
| abstract_inverted_index.obtained | 100 |
| abstract_inverted_index.proposed | 53 |
| abstract_inverted_index.received | 72, 157 |
| abstract_inverted_index.required | 13 |
| abstract_inverted_index.residual | 68 |
| abstract_inverted_index.transmit | 2 |
| abstract_inverted_index.weighted | 94 |
| abstract_inverted_index.wireless | 6 |
| abstract_inverted_index.algorithm | 65 |
| abstract_inverted_index.channels. | 48 |
| abstract_inverted_index.eliminate | 67 |
| abstract_inverted_index.estimator | 44, 55, 112, 122, 142 |
| abstract_inverted_index.reference | 149 |
| abstract_inverted_index.scenarios | 145 |
| abstract_inverted_index.M-estimate | 37, 59 |
| abstract_inverted_index.connection | 24 |
| abstract_inverted_index.difference | 130 |
| abstract_inverted_index.estimating | 15 |
| abstract_inverted_index.estimation | 78, 90, 98 |
| abstract_inverted_index.estimator, | 116 |
| abstract_inverted_index.normalized | 127 |
| abstract_inverted_index.robustness | 139 |
| abstract_inverted_index.simulation | 134 |
| abstract_inverted_index.Considering | 21 |
| abstract_inverted_index.estimation, | 32 |
| abstract_inverted_index.performance | 125 |
| abstract_inverted_index.traditional | 106 |
| abstract_inverted_index.cancellation | 11 |
| abstract_inverted_index.contaminated | 152 |
| abstract_inverted_index.demonstrates | 118 |
| abstract_inverted_index.simultaneous | 1 |
| abstract_inverted_index.transmission | 18 |
| abstract_inverted_index.multi-layered | 36 |
| abstract_inverted_index.reconstruction | 28 |
| abstract_inverted_index.single-layered | 114 |
| abstract_inverted_index.communications, | 7 |
| abstract_inverted_index.self-interference | 9 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |