Skip to main content

Electrical Engineering and Systems Science > Signal Processing

arXiv:1907.10519 (eess)
[Submitted on 22 Jul 2019]

Title:Modelling the Memory of Turbulence-Induced Beam Wander

Authors:Mitchell A. Cox, Lucas Gailele, Ling Cheng, Andrew Forbes
View PDF HTML (experimental)
Abstract:One of the major challenges for long range, high speed Free-Space Optical (FSO) communication is turbulence induced beam wander. Beam wander causes fluctuations in the received intensity as well as crosstalk in mode division multiplexed systems. Existing models for beam wander make use of probability distributions and long term averages and are not able to accurately model time-dependent intensity fluctuations such as deep fading, where the received intensity is too low to maintain reliable communication for an extended period of time. In this work we present an elegant new memory model which models the behaviour of beam wander induced intensity fluctuations with the unique capability to accurately simulate deep fading. This is invaluable for the development of optimised error correction coding and digital signal processing in order to improve the throughput and reliability of FSO systems.
Subjects: Signal Processing (eess.SP); Optics (physics.optics)
Cite as: arXiv:1907.10519 [eess.SP]
  (or arXiv:1907.10519v1 [eess.SP] for this version)
  https://doi.org/10.48550/arXiv.1907.10519

Submission history

From: Mitchell Cox Mr [view email]
[v1] Mon, 22 Jul 2019 12:10:16 UTC (4,567 KB)
Full-text links:

Access Paper:

view license

Current browse context:

eess.SP
< prev   |   next >
new | recent | 2019-07
Change to browse by:
eess
physics
physics.optics

References & Citations

Bookmark

BibSonomy Reddit

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?)
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?)