Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/9027
Title: | Simplified message transformation for optimization of message processing in 3G-324M control protocol |
Authors: | Dr. YUEN Man-Ching, Connie Shen, Ji Jia, Weijia Han, Bo |
Issue Date: | 2005 |
Publisher: | Springer |
Source: | Yuen, Man Ching, Shen, Ji, Jia, Weijia & Han, Bo (2005). Simplified message transformation for optimization of message processing in 3G-324M control protocol. In Lu, Xicheng & Zhao, Wei (Eds.). Networking and mobile computing : 3rd international conference, ICCNMC 2005, Zhangjiajie, China, August 2-4, 2005, proceedings. 3rd International Conference, ICCNMC 2005, Zhangjiajie, China (pp. 64-73). Springer. |
Conference: | International Conference on Networking and Mobile Computing 2005 |
Abstract: | 3G-324M is a multimedia transmission protocol designed for 3G communication environment. Meanwhile H.245 standard is a control protocol in 3G-324M and gives specific descriptions about terminal information messages in H.245 control channel as well as the procedures using them. The message syntax is defined using an external data representation standard called Abstract Syntax Notation One (ASN.1). For transmission, ASN.1 formatted data is transformed into bit-stream based on an ASN.1 encoding standard called Packed Encoding Rules (PER). In order to meet the requirement of high speed data transfer in 3G communication, it is important to design the procedure of message processing as simple as possible. In this paper, we propose Single-step Direct Message Transformation (SDMT)for the optimization of tree-structured message processing in H.245 module. By testing in realistic environments in some China industries, performance evaluation shows that code redundancies in terms of file size and code size are reduced significantly. |
Type: | Conference Paper |
URI: | http://hdl.handle.net/20.500.11861/9027 |
ISBN: | 9783540281023 9783540318682 |
DOI: | https://doi.org/10.1007/11534310_9 |
Appears in Collections: | Applied Data Science - Publication |
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