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A Low-power Convolutional Decoder with Error Detection Ability

In wireless communication systems, we may encounter many problems. One of the main issues is noise interference. To overcome the problem, the sender can use the Convolutional coding method to encode the data, and the receiver can utilize the Viterbi algorithm for decoding and correction purposes. Due to the high complexity of the Viterbi algorithm, the VLSI structure of Viterbi decoder will consume large amounts of power, leading the portable devices to short standby time and high operating temperature. In order to solve these problems we have to design a low power decoder.
As a matter of fact, the Viterbi decoder can be actually shutdown when no noise interference exists. As a consequence, we use a detection circuit to determine whether the signal is influenced by noise. If the signal is interfered, we choose the Viterbi decoder to perform the decoding process. Otherwise, we utilize a low cost decoder to lessen the power consumed at the receiver end.
In addition, dynamic adjustment of SMU module is also developed and implemented in the proposed decoder. SMU module consumes the most power in Viterbi decoder. So, our developed and goal is to reduce the usage of SMU module. If noise distribution is not so dense, we don¡¦t have to use high decoding ability to decode section data. Therefore, the registers in SMU can be decreased. Clock gating technique is adopted in this thesis to shutdown these idle registers to reduce the power consumption of SMU.
The proposed decoder has been implemented and synthesized using the Artisan TSMC 0.13£gm standard cell library. Compared with the traditional Viterbi decoder, the proposed decoder can achieve 25% and nearly 60% power saving when the SNR is 1dB and 8dB respectively, with 6% area reduction. According to the above experimental results, we can say that the proposed decoder is able to reduce power consumption.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0803110-152658
Date03 August 2010
CreatorsYeh, Wei-ting
ContributorsChuen-Yau Chen, Shiann-Rong Kuang, Ko-Chi Kuo, Shen-Fu Hsiao, Lih-Yang Wang
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-0803110-152658
Rightscampus_withheld, Copyright information available at source archive

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