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Channel Model And Synchronization Technology In Wireless Body Area Network

Posted on:2011-12-29Degree:MasterType:Thesis
Country:ChinaCandidate:N ShengFull Text:PDF
GTID:2178360308952515Subject:Communication and Information System
Abstract/Summary:PDF Full Text Request
In recent years, with the rapid development of wireless communication technology, as well as rising demand for wireless applications, the Wireless Body Area Network (WBAN) has gradually become a hot research topic of the next generation mobile communication technology. WBAN can be well applied to the field of medical, aerospace, sports and military, which implements the real-time measurement and monitoring of the human body.The typical WBAN system works around the human body, which may be influenced by the human body, especially the movement and the surrounding electromagnetic environment. Although the conventional wireless channel model and corresponding signal processing algorithms have been relatively mature, the research on the WBAN channel model is just beginning. And the channel model of WBAN is not mature enough which severely constrained the development of the corresponding synchronization, equalization technology and the system design.In order to solve the above problems, this paper designed the channel measurement scenario and method for the wireless channel around the human body and measured the corresponding channel characteristics for different frequencies in the indoor environment. Through the channel measurement result and corresponding research, the paper completed the channel model for the near-body-wireless channel by identifying the major factors which affected the wireless channel around the human body and focusing on the most significant factor– human motion.Based on the research result of channel model, this paper put forward the synchronization algorithm based on cognitive radio and synchronization algorithm using the special spectrum property of the preamble. Through theoretical analysis and verification on the hardware experimental platform, this algorithm can get good and stable performance in low SNR environment and it can also work stably in the near-body-wireless channel, which severely affected by the human motion. This has a guiding significance in the design and implementation of the WBAN.
Keywords/Search Tags:WBAN, Channel Model, Cognitive Radio, Synchronization
PDF Full Text Request
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