Efficient complexity reduction technique in trellis decoding algorithm

Satellite Commun. Syst. Dept., Electron. & Telecommun. Res. Inst., Daejeon
Electronics Letters (Impact Factor: 0.93). 02/1999; 35(1):16 - 17. DOI: 10.1049/el:19990081
Source: IEEE Xplore


An efficient reduced search trellis decoding algorithm in which
the decoder selects a part of existing paths by using a threshold value
of the path metric is proposed. The threshold value at each time stage
of the trellis is found by simply investigating the statistics of the
path metrics, and does not require any prior knowledge such as the
signal-to-noise ratio

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    ABSTRACT: In this paper, we propose an efficient reduced-search SOVA (soft output Viterbi algorithm) for block turbo codes. To reduce complexity, the decoder selects a part of the existing paths using the statistics of the path metrics. In addition, we compensate for the performance degradation incurred from soft output values at the parity part of the trellis that were too optimistically estimated, so that the performance of the reduced-search decoder almost approximates that of a full-search decoder. Our simulation results reveal that the proposed reduced-search method can reduce the complexity by about 10 times with just about 0.1dB performance degradation in coding gain.
    Communications, 2003. ICC '03. IEEE International Conference on; 06/2003
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    ABSTRACT: In this paper we propose a method that aims at reducing the complexity of convolutional and turbo decoding. Some calculations performed in decoding processing can be eliminated based on reliability thresholds. For convolutional and turbo decoding, the complexity is proportional to the number of branches in the trellis. For convolutional decoding, based on the Viterbi algorithm, we define reliability thresholds for the received samples of the signal and show that is possible to eliminate some branches in the trellis and consequently to reduce the complexity. For turbo decoding based on MAP algorithm, we set a threshold to classify each information bit log likelihood ratio (LLR). When the LLR is reliable, we take a decision on information bits and eliminate some branches in the trellis. Furthermore, we also define a criterion for stopping decoding wich further reduces the complexity. In this paper we show that it is possible to reduce decoding complexity of convolutional codes almost 80 % without performance degradation when compared to Viterbi algorithm over Rayleigh fading channels. In turbo decoding, we show that complexity varies with \({E_{b}}/{N_{0}}\) and it is reduced when more iterations are computed, tending to zero for higher iterations.
    Wireless Personal Communications 06/2015; 82(3). DOI:10.1007/s11277-015-2282-9 · 0.65 Impact Factor