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

Computer Science > Information Theory

arXiv:1609.09577 (cs)
[Submitted on 30 Sep 2016 (v1), last revised 10 Nov 2016 (this version, v3)]

Title:Numerical Approach to Maximize SNR for CDMA Systems

Authors:Hirofumi Tsuda, Ken Umeno
View PDF
Abstract:Signal to Noise Ratio (SNR) is an important index for wireless communications. There are many methods for increasing SNR. In CDMA systems, spreading sequences are used. To increase SNR, we have to improve spreading sequences. In classical approaches, the expression of SNR is not differentiable in terms of the parameter of the spreading sequences even in no fading situations. Thus, we express it as the differentiable form and construct the non-linear programing for maximizing SNR. In particular, we solve the problem of maximizing SNR numerically by obtaining spreading sequences whose SNR is guaranteed to be high.
Comments: 6 pages, 5 figures IEEE wcnc
Subjects: Information Theory (cs.IT)
Cite as: arXiv:1609.09577 [cs.IT]
  (or arXiv:1609.09577v3 [cs.IT] for this version)
  https://doi.org/10.48550/arXiv.1609.09577

Submission history

From: Hirofumi Tsuda [view email]
[v1] Fri, 30 Sep 2016 02:57:36 UTC (299 KB)
[v2] Mon, 17 Oct 2016 07:57:12 UTC (323 KB)
[v3] Thu, 10 Nov 2016 05:10:05 UTC (322 KB)
Full-text links:

Access Paper:

view license
Current browse context:
cs.IT
< prev   |   next >
new | recent | 2016-09
Change to browse by:
cs
math
math.IT

References & Citations

DBLP - CS Bibliography

listing | bibtex
Hirofumi Tsuda
Ken Umeno
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?)