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In this paper, we propose an efficient architecture based on pre-computation for Viterbi decoders incorporating T-algorithm. Through optimization at both. A Fast ACSU Architecture for Viterbi Decoder Using T-Algorithm. Jinjin He, Huaping Liu, Senior Member, IEEE, and Zhongfeng Wang*, Senior Member, IEEE. High performance ACS for Viterbi decoder using pipeline T-Algorithm .. Z. Wang, A fast ACSU architecture for Viterbi decoder using T-Algorithm, in: Proc.

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Hamming distance and Euclidean distance [10]. As a result, the de-coding speed of the low-power VD is greatly improved. Article Tools Print this article. The precomputation architecture that incorporates T-algorithm efficiently reduces the power consumption of VDs without reducing the decoding speed appreciably. It is essential to use T-algorithm in Viterbi decoders to prune significant portions of the trellis states to dramatically reduce power consumption.

Theoretically, when we continuously decompose Ps n-1Ps n-2 ,……, the precomputation scheme can be extended to Q steps. The states are further grouped into 4 clusters as described by 7. Through optimization at algorithm level greatly shortens the long critical path introduced by the T-algorithm. On the other hand the SST based scheme requires predecoding and re encoding process and is not suitable for TCM decoders.

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However, searching for the optimal path metric in the feedback loop still reduces the decoding acs. The use of convolutional codes with probabilistic decoding can significantly improve the error performance of a communication system [1].

Viterbi decoder Search for additional papers on this topic. Subscription Login to verify subscription. Email the author Login required. In other words, the states can be grouped into m clusters, where all the clusters have vitefbi same number of states and all the states in the same cluster will be extended by the same Bs.


Then, Bs are fed into the ACSU that recursively compute the path metrics Ps and outputs decision bits for each possible state transition. So, the computational over head and decoding latency due to predecoding and re encoding of the TCM signal become.

Abstract The viterbi t-akgorithm which is low power with convolution encoder is show in this paper. Synthesis and power estimation results are shown in section V. This architecture has been optimized to meet the iteration bound [9].

A fast ACSU architecture for Viterbi decoder using T-algorithm – Semantic Scholar

This is because the former decoder has a much longer critical path and the synthesis tool took extra measures to improve the clock speed. Showing of 6 references. At the receiver, a soft input VD should be employed to guarantee a good coding gain. Very Large Scale Integr. Most of vitegbi computational overhead comes from adding Bs to the metrics at each stage as indicated in 2. References Publications referenced by fot paper. The soft inputs of all VDs are quantized with 7 bits. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License.

Implementation of such a table is not trivial. It is worth to mention that the conventional T -algorithm VD takes slightly more hardware than the proposed architecture, which is counterintuitive. So, In terms of power consumption, the viterbi decoder is dominant module in a TCM decoder. We have also analyzed the precomputation algorithm, where the optimal precomputation steps are calculated and discussed.


Thus we next focus on the power comparison between the full trellis VD and the proposed scheme.

Sri lakshmi is currently working as an assi. This algorithm is suitable for TCM systems which always employ high-rate convolutional codes. Modern digital communication systems usually employ convolutional codes with large constraint length for good decoding performance, which leads to large complexity and power consumption in Viterbi decoders.

Low power Viterbi decoder for Trellis coded

If the target throughput is moderately high, fiterbi proposed architecture can operate at a lower supply voltage, which will lead to quadratic power reduction compared to the conventional scheme. Basically M-Algorithm requires a sorting process in a feedback loop where as T— Algorithm only searches for the optimal path metric [P] that is the maximum value or the minimum value of Ps.

The BMs are categorized in the same way and are described by 8. In addition, the computational overhead is a small. Computational overhead compared with conventional T-algorithm is an important factor that should be carefully evaluated. It is well known that viterbi decoder is dominant module for finding the overall power consumption for the T-algorithmm decoders.

NSP, digital communications by satellite. Typically a TCM system employs a high rate convolutional code, which leads to high complexity of viterbi decoder for the TCM decoder, when the constraint length of Convolutional code is also normal.

Critical path method Overhead computing Prunes. There are two different types of SMU in the literature: Seshasayanan International Conference on Information….