“…Step 2: Data bits are decoded by using the soft-decision Viterbi algorithm with truncation depth W [9] and decoding trellis length L + U. It is convenient to explain the Viterbi decoding algorithm by means of a trellis diagram.…”
Section: Circular Decoding Algorithmmentioning
confidence: 99%
“…This error probability is caused by both finite truncation depth and the uncertainty of the encoder's initial state. The bit error probability of the kth decoded bit is upper bounded by the sum of four upper bounds in (2), (7), (8), and (9).…”
Section: Upper Bounds On Error Probabilitiesmentioning
confidence: 99%
“…The rule of thumb for truncation depth has been studied in the literature [9,10], but never for higher order modulations on the Rayleigh channel. Several circular decoding algorithms with adaptive decoding trellis length were proposed in [11][12][13][14].…”
The performance of the wrap-around Viterbi decoding algorithm with finite truncation depth and fixed decoding trellis length is investigated for tail-biting convolutional codes in the mobile WiMAX standard. Upper bounds on the error probabilities induced by finite truncation depth and the uncertainty of the initial state are derived for the AWGN channel. The truncation depth and the decoding trellis length that yield negligible performance loss are obtained for all transmission rates over the Rayleigh channel using computer simulations. The results show that the circular decoding algorithm with an appropriately chosen truncation depth and a decoding trellis just a fraction longer than the original received code words can achieve almost the same performance as the optimal maximum likelihood decoding algorithm in mobile WiMAX. A rule of thumb for the values of the truncation depth and the trellis tail length is also proposed.
“…Step 2: Data bits are decoded by using the soft-decision Viterbi algorithm with truncation depth W [9] and decoding trellis length L + U. It is convenient to explain the Viterbi decoding algorithm by means of a trellis diagram.…”
Section: Circular Decoding Algorithmmentioning
confidence: 99%
“…This error probability is caused by both finite truncation depth and the uncertainty of the encoder's initial state. The bit error probability of the kth decoded bit is upper bounded by the sum of four upper bounds in (2), (7), (8), and (9).…”
Section: Upper Bounds On Error Probabilitiesmentioning
confidence: 99%
“…The rule of thumb for truncation depth has been studied in the literature [9,10], but never for higher order modulations on the Rayleigh channel. Several circular decoding algorithms with adaptive decoding trellis length were proposed in [11][12][13][14].…”
The performance of the wrap-around Viterbi decoding algorithm with finite truncation depth and fixed decoding trellis length is investigated for tail-biting convolutional codes in the mobile WiMAX standard. Upper bounds on the error probabilities induced by finite truncation depth and the uncertainty of the initial state are derived for the AWGN channel. The truncation depth and the decoding trellis length that yield negligible performance loss are obtained for all transmission rates over the Rayleigh channel using computer simulations. The results show that the circular decoding algorithm with an appropriately chosen truncation depth and a decoding trellis just a fraction longer than the original received code words can achieve almost the same performance as the optimal maximum likelihood decoding algorithm in mobile WiMAX. A rule of thumb for the values of the truncation depth and the trellis tail length is also proposed.
“…We are interested in the high SNR region, where the minimum distance terms dominate performance. A good indicator of the required truncation depth in this region is the path length at which all paths that diverge from a particular path have accumulated the minimum distance of the code, see, e.g., [10], [11], [5, pp. 262].…”
Section: Truncation Depths For Particular Codesmentioning
Abstract-The commonly used rule of thumb of 5m for the truncation depth of a memory m convolutional code is accurate only for rate 1/2 codes and should be replaced by two to three times m/(1 − r) for a rate r code.
“…The detected value of the coded symbol is then given by the earliest component of the survivor with the smallest cost. A delay in detection of typically 5L sampling intervals is normally assumed [75]. In Chapter 5, the Viterbi algorithm detector is described in detail.…”
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