A Spatial Diffusion Strategy for Tap-Length Estimation Over Adaptive Networks
- Submitting institution
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The University of Leicester
- Unit of assessment
- 12 - Engineering
- Output identifier
- 1518
- Type
- D - Journal article
- DOI
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10.1109/TSP.2015.2440182
- Title of journal
- IEEE Transactions on Signal Processing
- Article number
- -
- First page
- 4487
- Volume
- 63
- Issue
- 17
- ISSN
- 1053-587X
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2015
- URL
-
-
- Supplementary information
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https://doi.org/10.1109/TSP.2015.2440182
- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Distributed adaptive signal processing is extremely important in emerging communication and sensor networks. This work is profoundly significant as it is the first work to consider tap-length adaptive networks thereby introducing structural adaptation in the field. It builds upon a long-history of world-leading research activity in distributed adaptive signal processing, resulting in the award in 2017 of funding to Prof Chambers from the highly prestigious 1000 Talents Programme and also stimulated new work with Professor Ali Sayed, FIEEE, in the Adaptive Systems Laboratory at UCLA, now at EPFL {ali.sayed@epfl.ch}.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -