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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Email the author Login required. 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.
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Low power Viterbi decoder for Trellis coded
For VD in-corporated with T- algorithm, no state is guaranteed to be active at all clock cycles. The functional block architcture of viterbi decoder with two step precomputation T-algorithm is shown in fig.
The precomputation architecture that incorporates T-algorithm efficiently reduces the power consumption of VDs without reducing the decoding speed appreciably.
In order to further shorten the critical path, we explore the 2-step pre-computation design next.
From This Paper Figures, tables, and topics from this paper. Very Large Scale Dceoder. 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. Table IV shows that, as the threshold decreases, the power. It is worth to mention that t-algodithm conventional T -algorithm VD takes slightly more hardware than the proposed architecture, which is counterintuitive.
A fast ACSU architecture for Viterbi decoder using T-algorithm – Semantic Scholar
Implementation of Viterbi coder for text to speech synthesis M. Even if the extra delay is deoder to eliminate, the resultant clock speed is usibg close to the theoretical bound. This architecture has been optimized to meet the iteration bound . Firstbranch metrics are calculated in the B unit BMU from the received symbols.
Theoretically, when we continuously decompose Ps n-1Ps n-2 ,……, the precomputation scheme can be extended to Q steps. The trellis butterflies for a VD usually have a symmetric structure.
A fast ACSU architecture for Viterbi decoder using T-algorithm
Topics Discussed in This Paper. After that, the decision bits usinv stored in and retrieved from the SMU in order to decode the source bits along the final survivor path.
The minimum P becomes:. Then, Bs are fed into the ACSU that recursively compute the path metrics Ps and outputs decision bits for each possible state transition. Synthesis and power estimation results are shown in section V. In some cases, the number archiitecture remaining metrics may slightly expand during a certain pipeline stage after addition with Bs. To overcome this uaing, T-Algorithm has proposed in two variations, the relaxed adaptive VD , 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].
To fully achieve the iteration bound, we could add another pipeline stage, though it is very costly. Therefore, for high-speed applications, it should not be considered. There are two different types of SMU in the literature: Design of high speed low power viterbi decoder for TCM system J.
It is essential to use T-algorithm in Viterbi decoders to prune significant portions of the trellis states to dramatically reduce power consumption. Again, to simplify the evaluation, we consider, acsh code with a constraint length k and q precomputation steps. Later in the next section we will report ASIC implementation results that have not been obtained earlier.
However, searching for the optimal path metric in the feedback loop still reduces the decoding speed. At the receiver, a soft input VD should be employed to guarantee a good t-altorithm gain. The functional diagram of the 1-step pre-computation scheme is shown in Fig. References Publications referenced by this viferbi.
Implementation of such a table is not trivial. The BMs are categorized in the same way and are described by 8. X 1 0 ………………………. Finally, we presented a design case. Therefore, the hardware overhead of architecturw proposed VD is expected.