Resources and Help A fast ACSU architecture for Viterbi decoder using T-algorithm Abstract: 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. It is essential to use T-algorithm in Viterbi decoders to prune significant portions of the trellis states to dramatically reduce power consumption. However, the operation of searching for the best path metrics in the add-compare-select loop in T-algorithm significantly limits the clock speed. In this paper, we propose an efficient architecture based on pre-computation for Viterbi decoders incorporating T-algorithm. Through optimization at both algorithm level and architecture level, the new architecture greatly shortens the long critical path introduced by the conventional T-algorithm.

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On the other hand the SST based scheme requires predecoding and re encoding process and is not suitable for TCM decoders. For clarity, we only provide the main conclusion here. Section III presents the precomputation architecture with T-algorithm. Also, we assume that each remaining metric would cause a computational overhead of one addition operation.

In other words, the states can be grouped into m clusters, where all the clusters have the same number of states and all the states in the same cluster will be extended by the same Bs.

The use of convolutional codes with probabilistic decoding can significantly improve the error performance of a communication system [1].

Design of high speed low power viterbi decoder for TCM system J. Power reduction in VDs could be achieved by reducing the number of states, for example reduced state sequence decoding [3], M- algorithm [4] and T-algorithm [1],[5], or by over scaling the. Therefore, a small number of precomputational steps is preferred even though the iteration bound may not be fully satisfied. Very large scale integration VLSI systems. In order to reduce the computational complexity as well as power consumption, low power schemes should be exploited for the VD in a TCM decoder.

In most cases, one or two-step precomputation is a good choice. A general diagram for a viterbi decoder is shown in fig. Consider a VD for a convoluional code with a constraint length k, where each state receives p candidate paths.

When applied to high rate convolutional codes, the relaxed adaptive VD suffers a severe degradation of bit- error-rate BER performance due to the inherent usinh error between the estimated optimal path metric and the accurate one[9].

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. Even if the extra delay is hard to eliminate, the resultant clock speed is very close to the theoretical bound. Foe trellis butterflies for a VD usually have a symmetric structure.

Low power Viterbi decoder for Trellis coded Modulation using T-algorithm This architecture has been optimized to meet the iteration bound [9]. The results are shown in Table IV. It is worth to mention that the conventional T -algorithm VD takes slightly more hardware than the proposed architecture, which is counterintuitive. The BMs are t-algotithm in the same way and are described by 8. Since the performance is the same as that of the conventional T-algorithm.

There are two cor types of SMU in the literature: X 1 0 ………………………. This paper has 27 citations. The precomputation architecture archktecture incorporates T-algorithm efficiently reduces the power consumption of VDs without reducing the decoding speed appreciably.

Furthermore, the even states all extend to states with higher indices the MSB in Fig. Finally, we presented a design case. This is because the former decoder has a much longer critical path and the synthesis tool took extra measures to improve the clock speed.

In order to further shorten the critical path, we explore the 2-step pre-computation design next. The architecture of TGU is shown in fig. Each PM in all VDs is quantized as 12 bits. Suppose that we have labeled the states from 0 to Then, Bs are fed into the ACSU that recursively compute the path metrics Ps and outputs decision bits for each possible state transition.

References Publications referenced by this paper. The output of the priority t-aogorithm would be the unpurged state with the lowest index. Computational overhead compared with conventional T-algorithm is an important factor that should be carefully evaluated. The Branch metric can be calculated by two types: Email this article Login required. Viterbi Convolutional Encoding and Viterbi Decoding.

IC design of an adaptive Viterbi decoder. Over scaling of the supply voltage is having a problem that it needs to take whole system into consideration including with VD at which we are not focusing of our research. General solutions for low power viterbi decoder design will be studied in our implementation work. Therefore, for high-speed applications, it should not be considered.

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

On the other hand, the VD with conventional T- algorithm cannot achieve half of the clock speed of the full trellis VD. TOP Related Posts.

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## A FAST ACSU ARCHITECTURE FOR VITERBI DECODER USING T-ALGORITHM PDF

On the other hand the SST based scheme requires predecoding and re encoding process and is not suitable for TCM decoders. For clarity, we only provide the main conclusion here. Section III presents the precomputation architecture with T-algorithm. Also, we assume that each remaining metric would cause a computational overhead of one addition operation. In other words, the states can be grouped into m clusters, where all the clusters have the same number of states and all the states in the same cluster will be extended by the same Bs. The use of convolutional codes with probabilistic decoding can significantly improve the error performance of a communication system [1].

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