Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Mathematics > Optimization and Control

arXiv:2108.13189 (math)
[Submitted on 30 Aug 2021]

Title:Underwater Acoustic Channel Adaptive Estimation using l21 Norms

Authors:Amer Aljanabi, Mohanad Abd Shehab, Osama Alluhaibi, Qasim Zeeshan Ahmed, Pavlos Lazaridis
View PDF
Abstract:The problem of underwater acoustic (UWA) channel estimation is the non-uniform sparse representation that may increase the algorithm complexity and the required time. A mathematical framework utilizing l21 constraint with two-dimensional frequency domain is employed to enhance the channel estimation. The frame work depends on both main and auxiliary channel information. The simulation results have been demonstrated that the proposed estimation method can improve some problems that are achieved with other norms like l1. Furthermore, it can achieve a better performance in terms of mean square error (MSE) and execution time.
Subjects: Optimization and Control (math.OC)
Cite as: arXiv:2108.13189 [math.OC]
  (or arXiv:2108.13189v1 [math.OC] for this version)
  https://doi.org/10.48550/arXiv.2108.13189

Submission history

From: Qasim Ahmed [view email]
[v1] Mon, 30 Aug 2021 12:53:21 UTC (335 KB)
Full-text links:

Access Paper:

  • View PDF
  • Other Formats
view license
Current browse context:
math.OC
< prev   |   next >
new | recent | 2021-08
Change to browse by:
math

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)