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.

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Mekazahn RE scheme with survival length of 42 is used for SMU and the register arrays associated with the purged states are clock-gated to reduce the power consumption in SMU. On the other hand, the VD with conventional T- g-algorithm cannot achieve half of the clock speed of the full trellis VD. The operation of searching for the best viterrbi metrics in the add-compare-select loop in T-algorithm significantly limits the clock speed.

In a real design the overhead increases even faster than what is given by 5 when other factors such as comparisons or t-alyorithm of metrics as we mentioned above are taken into consideration.

The 64 states and path metrics are labeled from 0 to For the convenience of our discussion we define the left most register in Fig. Then, Bs are fed into the ACSU that recursively compute the path metrics Ps and outputs decision bits for each possible state transition. The use of convolutional codes with probabilistic decoding can significantly improve the error performance of a communication system [1]. Abdul SubhanDikpal Reddy It is clear that the conventional T- algorithm is not suitable for high-speed applications.

First, we expand Ps at the current time slot n Ps n as a function of Ps n-1 to form a look-ahead computation of the optimal P-Popt n. Therefore, the hardware overhead of the proposed VD is expected. In this section, we address an important issue regarding SMU design when T -algorithm is employed.

Section III presents the precomputation architecture with T-algorithm. Hamming distance and Euclidean distance [10]. Where q is any arcchitecture integer that is less than n.

The output of the priority encoder would be the unpurged state with the lowest index. Citations Publications citing this paper. In [9], through a design example that, drcoder -step uding computation can be pipelined into q stages, where the logic delay of each stage is continuously reduced as q increases.

In most cases, one or two-step precomputation is a good choice. Viterbii 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. To overcome this drawback, T-Algorithm has proposed in two variations, the relaxed adaptive VD [7], Which suggests using an estimated optimal path metric, instead of finding the real one each cycle and the limited-search parallel state VD based on scarce state transition [SST][8].

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 vietrbi same cluster will be extended by the same Bs. In order to further shorten the critical path, we explore the 2-step pre-computation design next. After that, the decision bits are stored in and retrieved from the SMU in order archktecture decode the source bits along the final survivor path.

Through optimization at algorithm level greatly shortens the long critical path introduced by the T-algorithm.

Even if the vitergi delay is hard to eliminate, the resultant clock speed is very close to the theoretical bound. Again, to simplify the evaluation, we consider, a code with a constraint length k and q precomputation steps. Table IV shows that, as the threshold decreases, the power. Viterbi decoder Viterbi algorithm Convolutional code Clock rate Computation. T-algoriithm, a small number of precomputational steps is preferred even though the iteration bound may not be fully satisfied. However, searching for the optimal path metric in the feedback loop still reduces the decoding speed.

In addition, the computational overhead is a small. A total of received symbols 12 bits are simulated. From This Paper Figures, tables, and topics from this paper. The soft inputs of all VDs are quantized with 7 bits. A general diagram for a viterbi decoder is shown in fig. Implementation of Viterbi coder for text to speech synthesis M. Showing of 20 extracted citations. The minimum P becomes:. This is because the former decoder has a much longer critical path and the synthesis tool took extra measures to improve the clock speed.

How to cite t-alborithm. Basically M-Algorithm requires a sorting process in a feedback loop where as Tâ€” Algorithm only searches for the optimal path metric [P] that is the maximum value or the minimum value of Ps. The Ps of the current iteration are stored in the path metric unit PMU. Related Articles.

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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.

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