On the efficient search of punctured convolutional codes with simulated annealing algorithm

doi:10.1016/S1005-8885(14)60288-0

中国邮电高校学报(英文) ›› 2014, Vol. 21 ›› Issue (2): 69-74.doi: 10.1016/S1005-8885(14)60288-0

On the efficient search of punctured convolutional codes with simulated annealing algorithm

邹卫霞,王振宇,王桂叶,杜光龙, GAO Ying

1. Key Laboratory of Wireless Universal Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China 2. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

收稿日期:2013-08-22 修回日期:2013-12-31 出版日期:2014-04-30 发布日期:2014-04-30
通讯作者: 王振宇 E-mail:wzy221@gmail.com
基金资助:
基于群体智能的多Agent协作模型与适应性行为研究

On the efficient search of punctured convolutional codes with simulated annealing algorithm

1. Key Laboratory of Wireless Universal Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China 2. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2013-08-22 Revised:2013-12-31 Online:2014-04-30 Published:2014-04-30
Supported by:
the National Natural Science Foundation of China (61171104).

摘要/Abstract

摘要：

Punctured convolution codes (PCCs) have a lot of applications in modern communication system. The efficient way to search for best PCCs with longer constraint lengths is desired since the complexity of exhaustive search becomes unacceptable. An efficient search method to find PCCs is proposed and simulated. At first, PCCs’ searching problem is turned into an optimization problem through analysis of PCCs’ judging criteria, and the inefficiency to use pattern search (PS) for many local optimums is pointed out. The simulated annealing (SA) is adapted to the non-convex optimization problem to find best PCCs with low complexity. Simulation indicates that SA performs very well both in complexity and success ratio, and PCCs with memories varying from 9 to 12 and rates varying from 2/3 to 4/5 searched by SA are presented.

关键词:

PCCs, optimization problem, pattern search, simulated annealing

Abstract:

Key words:

PCCs, optimization problem, pattern search, simulated annealing

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On the efficient search of punctured convolutional codes with simulated annealing algorithm

On the efficient search of punctured convolutional codes with simulated annealing algorithm

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