Return to search

Low-Power Adaptive Viterbi Decoder with Section Error Identification

In wireless communication system, convolutional coding method is often used to encode the data. In decoding convolutional code (CC), Viterbi algorithm is considered to be the best mechanism. Viterbi decoder (VD) was developed to execute the algorithm on mobile devices more effectively. This decoder is often used on 2G and 3G mobile phones. However, on 2G phones, VD consumes about one third of total power consumption of the signal receiver. Therefore it is very necessary to reduce the power consumption of VD on 2G and 3G phones.
VD uses large amount of register in survivor metric unit (SMU), so that the decoder can receive enough CC and converge automatically. The goal of this thesis is to decrease power consumption of SMU by using path metric compare unit (PMCU) to find the best state of path metric unit (PMU). This way decreases half of registers and multiplexers required in SMU, leading to significant area reduction in decoder. During the process of signal transmission in wireless communication, different causes like the atmosphere, outer space radiation and man-made will interfere the signal by different degree. The stronger the noise is, the more interference CC will get.
The error detection circuit used will mark the sections with noise interference before the CC enters the VD. If CC is interfered, it will be decoded by the whole VD. Otherwise, it will be decoded by low power decoder, where the controller will start clock gating mechanism on SMU to close up unnecessary power consumption block.
The power consumption of is varying proposed Adaptive Viterbi decoder according to the interference degree. When interference degree is high, the power consumption is 21% less than conventional VD; when interference is low, it is 44% less. The results show that the proposed method can effectively reduce the power consumption of VD.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0728111-180932
Date28 July 2011
CreatorsLi, Shih-Jie
ContributorsShen-Fu Hsiao, Ming-Chih Chen, Yun-Nan Chang, Shiann-Rong Kuang, Ko-Chi Kuo
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0728111-180932
Rightsuser_define, Copyright information available at source archive

Page generated in 0.0087 seconds