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

Electrical Engineering and Systems Science > Signal Processing

arXiv:2101.11348 (eess)
[Submitted on 27 Jan 2021 (v1), last revised 2 Nov 2021 (this version, v3)]

Title:Low-Complexity Joint CFO and Channel Estimation for RIS-aided OFDM Systems

Authors:Sumin Jeong, Arman Farhang, Nemanja Stefan Perović, Mark Flanagan
View PDF
Abstract:Accurate channel estimation is essential for achieving the performance gains offered by reconfigurable intelligent surface (RIS)-aided wireless communications. A variety of channel estimation methods have been proposed for such systems; however, none of the existing methods takes into account the effect of synchronization errors such as carrier frequency offset (CFO). In general, CFO can significantly degrade channel estimation performance of orthogonal frequency division multiplexing (OFDM) systems. Motivated by this, we investigate the effect of CFO on channel estimation for RIS-aided OFDM systems. Furthermore, we propose a joint CFO and channel impulse response (CIR) estimation method for these systems. Simulation results demonstrate the effectiveness of our proposed method, and also demonstrate that the use of time-domain rather than frequency-domain estimation in this context results in an improvement in the mean-squared error (MSE) performance of channel estimation as well as a significantly lower overall computational complexity.
Comments: 5 pages, 5 figures, this manuscript has been submitted to IEEE WCL and accepted (Digital Object Identifier:https://doi.org/10.1109/LWC.2021.3124049)
Subjects: Signal Processing (eess.SP)
Cite as: arXiv:2101.11348 [eess.SP]
  (or arXiv:2101.11348v3 [eess.SP] for this version)
  https://doi.org/10.48550/arXiv.2101.11348
Journal reference: IEEE Wireless Communications Letter Volume: 11, Issue: 1, Jan. 2022 Page(s): 203 - 207
Related DOI: https://doi.org/10.1109/LWC.2021.3124049

Submission history

From: Sumin Jeong [view email]
[v1] Wed, 27 Jan 2021 12:23:49 UTC (1,943 KB)
[v2] Thu, 18 Mar 2021 15:48:10 UTC (1,576 KB)
[v3] Tue, 2 Nov 2021 20:51:16 UTC (566 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
eess.SP
< prev   |   next >
new | recent | 2021-01
Change to browse by:
eess

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?)