Bidirectional breadth-first algorithms for the decoding of convolutional codes

Belzile, J.; Haccoun, D.

doi:10.1109/26.216512

Cited by 25 publications

(12 citation statements)

References 8 publications

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“…However, only the estimate of the CIR is available in an adaptive MLSDE and the value of depends on the state and branch metric, which are assumed to be the correct ones. When the estimation is reliable, one can substitute , the estimate of , instead of in (10); then the expectation of can be approximated by (11) where denotes the conjugate of . can be calculated by assuming that all states have the same probability of being the correct state (before selecting some of them as the more likely correct states).…”

Section: A Adaptive Threshold (At) Methodsmentioning

confidence: 99%

“…In developing (10), it was assumed that the CIR, , was known and is a deterministic vector. However, only the estimate of the CIR is available in an adaptive MLSDE and the value of depends on the state and branch metric, which are assumed to be the correct ones.…”

Section: A Adaptive Threshold (At) Methodsmentioning

confidence: 99%

“…In the T-algorithm, neither the number of states in the selected subset nor the states allocated to the selected subset is fixed. Based on this approach, other algorithms such as the modified RSSE [9], the bidirectional tree searching algorithm [10], the trellis search algorithm [11] and the reduced state decision feedback estimation algorithm [12] have been proposed. In [11], motivated by the M-algorithm, the computational complexity of a blind joint channel estimation and data detection receiver has been reduced and in [12] the T-algorithm is used for reducing the computational complexity of a suboptimal symbol-by-symbol detection scheme.…”

Section: Introductionmentioning

confidence: 99%

“…It should be mentioned that for computing the exact value of the threshold [such as(10)] knowing the ith state and the CIR are necessary; only for calculating its maximum (19) such knowledge is not necessary.…”

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confidence: 99%

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Complexity reduction of the MLSD/MLSDE receiver using the adaptive state allocation algorithm

Zamiri-Jafarian

Pasupathy

2002

IEEE Trans. Wireless Commun.

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The idea of adaptive state allocation (ASA) algorithm is used in this paper to substantially reduce the computational complexity of the maximum-likelihood sequence detection and estimation (MLSD/MLSDE) receiver without a significant degradation in its performance. In the ASA algorithm, the total number of states assigned to the trellis and the number of states selected from the entire set are changed adaptively based on the short-term power of the channel impulse response (CIR) or its estimate.The ASA algorithm is a combination of two methods: adaptive threshold (AT) and adaptive state partitioning (AP). In the AT method, a threshold value is formulated based on the probability of removing the correct state in the trellis diagram. At each time, only the paths whose costs are less than the minimum cost (corresponding to the best survivor path) plus the threshold value are retained and are extended to the next trellis stage. The AT method significantly reduces the computational complexity of the regular MLSDE mostly at high signal-to-noise ratio (SNR) with a negligible loss in performance. Simulation results for fading channels show that the AT method typically selects one trellis state (the minimum possible number of states) at high SNRs. In the AP method, the branch metrics are fused and diffused adaptively by using the Kullback-Leibler (KL) distance metric invoked for quantifying the differences between the probability density functions of the correct and incorrect branch metrics in the trellis. The adaptation is done such that the channel coefficients with short-term power less than a threshold are assumed to be zero in computing the branch metrics. The AP method decreases the computational complexity of the regular MLSDE at low SNRs.

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Section: A Adaptive Threshold (At) Methodsmentioning

confidence: 99%

Section: A Adaptive Threshold (At) Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Complexity reduction of the MLSD/MLSDE receiver using the adaptive state allocation algorithm

Zamiri-Jafarian

Pasupathy

2002

IEEE Trans. Wireless Commun.

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“…4]. Efficient methods for this calculation include the recursive algorithm of Divsalar et al [46], or a breadth first search algorithm [47].…”

Section: A Generalized Weight Distribution Spectrummentioning

confidence: 99%

Exploiting UEP in QAM-based BICM: interleaver and code design

Alvarado

Agrell

Szczeciński

et al. 2010

IEEE Trans. Commun.

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In this paper we formally analyze the interleaver and code design for QAM-based BICM transmissions using the binary reflected Gray code. We develop analytical bounds on the bit error rate and we use them to predict the performance of BICM when unequal error protection (UEP) is introduced by the constellation labeling. Based on these bounds the optimum design of interleaver and code is found, and numerical results for representative configurations are presented. When the new design is used, the improvements may reach 2 dB, and they are obtained without complexity increase. We also introduce the concept of generalized optimum distance spectrum convolutional codes, which are the optimum codes for this scenario. Index TermsBICM, interleaver design, multiple interleaver, optimum distance spectrum codes, QAM, UEP.

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