Ms. Mengyuan Li Profile

Mengyuan Li

at Beijing Jiaotong Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 15 November 2018 Paper

Enhanced performance of high-speed IM/DD DMT systems using an EM channel estimation algorithm for short reach optical communication

Mengyuan Li, Muguang Wang, Qi Ding

Proceedings Volume 10964, 109646F (2018) https://doi.org/10.1117/12.2506529

KEYWORDS: Expectation maximization algorithms, Digital signal processing, Optical communications, Modulation, Receivers, Telecommunications, Signal to noise ratio, Transmitters, Computer simulations, Error analysis

Read Abstract +

A semi-blind Expectation-Maximization (EM) channel estimation algorithm is proposed for 50 Gb/s quadrature phase shift keying (QPSK)-Discrete MultiTone (DMT) signal transmission systems using intensity modulation/direct detection (IM/DD) over 100 km standard single mode fiber (SSMF). The reported channel estimation methods for DMT systems can be roughly divided into two categories: semi-blind channel estimation and blind channel estimation. Due to the low accuracy of traditional blind channel estimation algorithm and lower spectral efficiency of the semi-blind channel estimation algorithm relying on more training sequences (TS), EM algorithm is proposed that is a two-step iterative procedure to maximize the likelihood function for achieving channel estimation instead of classical semi-blind channel estimation methods with more TS. Also, we assume the channel of the IM/DD DMT system as an additive white gaussian noise (AWGN) channel. Simulation results show that using EM algorithm yields about 2 dB optical signal noise ratio (OSNR) improvement at a bit error ratio (BER) of 3.8×10^-3 compared to classical channel estimation based on TS under the same number of TS and has the similar performance compared to classical channel estimation relying on more TS. In addition, it is shown that at high OSNR (>19 dB), the performance of EM algorithm outperforms that of LMS algorithm. On the contrary, the performance of least mean square (LMS) algorithm outperforms that of EM algorithm at low OSNR (<19 dB).

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years