By Topic

Modulation classification of MQAM signals using particle swarm optimization and subtractive clustering

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Li Yan-ling ; Sch. of Inf. & Manage. Sci., Henan Agric. Univ., Zhengzhou, China ; Li Bing-bing ; Yin Chang-yi

This paper proposes a novel algorithm for modulation recognition of MQAM signals which uses the combination of subtractive clustering (SC) and particle swarm optimization (PSO) (PSO-SC) to extract the discriminating features. The method uses PSO to search for the best clustering radius of SC in order to enable reconstructed constellation optimal. Then, the best clustering radius (CR) is used as classification feature. Compared with the classification methods available using subtractive clustering, the algorithm proposed by this paper has higher correct classification rate in modulation classification for MQAM signals. In addition, simulation results show that the modulation classification method performs robust in the low signal-noise ratio.

Published in:

Signal Processing (ICSP), 2010 IEEE 10th International Conference on

Date of Conference:

24-28 Oct. 2010