A truncation depth rule of thumb for convolutional codes

Moision, Bruce

doi:10.1109/ita.2008.4601052

Cited by 15 publications

(5 citation statements)

References 12 publications

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“…Let code rate r = 1/2 and n = 256, and let the code be of the cyclic redundancy code (CRC)-aided kind with 24 CRC bits attached to the information sequence. We also apply the bit interleaving in [7, §5.4.1.3], which legitimizes the approximation in (42). At the receiver, we employ successive cancellation list decoding (SCLD) with different list sizes for different decoding complexities.…”

Section: Some Numerical Resultsmentioning

confidence: 99%

“…In the case of burst-error codes, such as a convolutional code, it is known that the Viterbi decoding delay has much to do with decoding performance and hence should serve well to define the subsystem dimension. More generally, any segmentation of the received signal may serve this purpose as long as the It is known that for a rate-1/2 convolutional code, Viterbi decoding yields near ML performance at decoding delays on the order of 5 constraint lengths [32], [42]. Hence typical burst errors under proper convolutional decoding would not exceed this duration.…”

Section: Extension To Burst-error Codesmentioning

confidence: 99%

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Fast Simulation of Coded QAM Transmission in White Gaussian Noise at Low Packet Error Rates

Lin

Sang

2022

IEEE Open J. Commun. Soc.

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Today's communication system design heavily depends on computer simulation for performance evaluation. The burgeoning ultra-reliable communication systems, however, pose a significant simulation challenge as such systems operate at very low packet error rates (PERs) whereas the required simulation time of the conventional Monte Carlo (MC) method is many times the inverse of the PER. Various importance sampling-type techniques have been developed for more efficient simulation of channelcoded transmission, but they typically rely on exploiting code weaknesses (for the generation of errorcausing noise samples) and most works on soft-decision decoding only treat binary signaling. In this paper, we propose to use a function, termed the noise gauging function (NGF), that roughly measures the error-causing propensity of noise samples and we present a way to adaptively optimize the noise sampling under such a function for simulation efficiency. Both binary and nonbinary signalings are considered. And the proposed technique does not require detailed knowledge of the code weaknesses, although some high-level understanding of the code properties can benefit the design of efficient NGFs. We investigate the application of the proposed technique to several common channel codes. Numerical results indicate an approximately 10-to 1,000-fold speedup versus MC.INDEX TERMS BCH codes, channel coding, convolutional codes, fast simulation, importance sampling, Monte Carlo, packet error rate, polar codes, ultra reliable and low latency communication (URLLC).

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Section: Some Numerical Resultsmentioning

confidence: 99%

Section: Extension To Burst-error Codesmentioning

confidence: 99%

Fast Simulation of Coded QAM Transmission in White Gaussian Noise at Low Packet Error Rates

Lin

Sang

2022

IEEE Open J. Commun. Soc.

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“…The actual decoding delay of symbols into data can be found by tracing the maximum likelihood path backwards through the trellis. In practice, the decoder starts to decode bits once it has reached a time equal to five times the constraint length 5K = 5(3) = 15, [15], [16], while in our proposed Kronecker-RoD case, the delay is dependent on the size of the block which is L = 16.…”

Section: Numerical Resultsmentioning

confidence: 99%

Rank-one Detector for Kronecker-Structured Constant Modulus Constellations

Fazal-E-Asim¹,

Almeida²,

Haardt³

et al. 2020

Preprint

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To achieve reliable communication is indispensable for the high quality of service required by 5G. We propose a novel decoding strategy for constant modulus signals which is useful to provide low bit error ratios (BERs) in the low energy per bit to noise power spectral density ratio (Eb/No) regime with efficient computational complexity. The proposed strategy enjoys the benefit of exploiting the transmission of symbols as Kronecker products at the transmitter side. The proposed Kronecker-RoD (rank-one detector) is compared with convolutional codes with hard and soft Viterbi decoding. We observe that the Kronecker-RoD outperforms the latter in BER performance and has an efficient implementation complexity.

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“…code sequence properties [12], and experimentation [22], [23]. A reasonable rule of thumb for a rate-r code with memory ν is…”

Section: Detector Implementation Detailsmentioning

confidence: 99%

PR-NN: RNN-based Detection for Coded Partial-Response Channels

Zheng

Liu

Siegel

2020

Preprint

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In this paper, we investigate the use of recurrent neural network (RNN)-based detection of magnetic recording channels with inter-symbol interference (ISI). We refer to the proposed detection method, which is intended for recording channels with partial-response equalization, as Partial-Response Neural Network (PR-NN). We train bi-directional gated recurrent units (bi-GRUs) [7], [30] to recover the ISI channel inputs from noisy channel output sequences and evaluate the network performance when applied to continuous, streaming data. The computational complexity of PR-NN during the evaluation process is comparable to that of a Viterbi detector. The recording system on which the experiments were conducted uses a rate-2/3, (1,7) runlength-limited (RLL) code [37] with an E 2 PR4 partial-response channel target. Experimental results with ideal PR signals show that the performance of PR-NN detection approaches that of Viterbi detection in additive white gaussian noise (AWGN). Moreover, the PR-NN detector outperforms Viterbi detection and achieves the performance of Noise-Predictive Maximum Likelihood (NPML) detection in additive colored noise (ACN) at different channel densities. A PR-NN detector trained with both AWGN and ACN maintains the performance observed under separate training.Similarly, when trained with ACN corresponding to two different channel densities, PR-NN maintains its performance at both densities. Experiments confirm that this robustness is consistent over a wide range of signal-to-noise ratios (SNRs). Finally, PR-NN displays robust performance when applied to a more realistic magnetic recording channel with MMSE-equalized Lorentzian signals.

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A truncation depth rule of thumb for convolutional codes

Abstract: 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.

Cited by 15 publications

References 12 publications

Fast Simulation of Coded QAM Transmission in White Gaussian Noise at Low Packet Error Rates

Fast Simulation of Coded QAM Transmission in White Gaussian Noise at Low Packet Error Rates

Rank-one Detector for Kronecker-Structured Constant Modulus Constellations

PR-NN: RNN-based Detection for Coded Partial-Response Channels

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