A Low Latency Coding Scheme: Semi-Random Block Oriented Convolutional Code

Lin, Wenchao; Cai, Suihua; Sun, Jiachen; Ma, Xiao; Wei, Baodian

doi:10.1109/istc.2018.8625272

Cited by 7 publications

(8 citation statements)

References 13 publications

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“…As a kind of convolutional codes with large constraint length, the SRBO-CCs are typically nondecodable by VA. In [22], the authors analyze the tree structure of the SRBO-CCs and employ the sub-optimal sequential decoding, whose memory load is heavy due to the requirement of a large amount of stack memory.…”

Section: Successive Cancellation Decodingmentioning

confidence: 99%

“…To solve this issue, the semi-random block oriented convolutional code (SRBO-CC) was proposed in [22] with a Cartesian product of short code as the basic code. The SRBO-CCs can be decoded by the sequential decoding, whose memory load is heavy due to the requirement of a large amount of stack memory.…”

Section: Introductionmentioning

confidence: 99%

“…In [24], the TBCCs were taken as the basic codes to improve the performance. As extension works of [22] [23] [24], we present in this paper more details on the SRBO-CCs. The SRBO-CCs are decoded by a sliding window decoding algorithm with successive cancellation, whose performance depends critically on the performance of the first sub-frame.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Successive Cancellation List Decoding of Semi-random Unit Memory Convolutional Codes

Lin¹,

Cai²,

Wei³

et al. 2019

Preprint

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In this paper, we propose a statistical learning aided list decoding algorithm, which integrates a serial list Viterbi algorithm (SLVA) with a soft check instead of the conventional cyclic redundancy check (CRC), for semi-random block oriented convolutional codes (SRBO-CCs). The basic idea is that, compared with an erroneous candidate codeword, the correct candidate codeword for the first sub-frame has less effect on the output of Viterbi algorithm (VA) for the second sub-frame. The threshold for testing the correctness of the candidate codeword is then determined by learning the statistical behavior of the introduced empirical divergence function (EDF). With statistical learning aided list decoding, the performance-complexity tradeoff and the performance-delay tradeoff can be achieved by adjusting the statistical threshold and extending the decoding window, respectively. To analyze the performance, a closed-form upper bound and a simulated lower bound are derived. Simulation results verify our analysis and show that: 1) The statistical learning aided list decoding outperforms the sequential decoding in high signal-to-noise ratio (SNR) region; 2) under the constraint of equivalent decoding delay, the SRBO-CCs have comparable performance with the polar codes.

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Section: Successive Cancellation Decodingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Successive Cancellation List Decoding of Semi-random Unit Memory Convolutional Codes

Lin¹,

Cai²,

Wei³

et al. 2019

Preprint

Self Cite

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show abstract

“…To solve this issue, the semi-random block oriented convolutional code (SRBO-CC) was proposed in [16] with a Cartesian product of short code as the basic code. The SRBO-CC can be decoded by the sequential decoding, whose memory load is heavy due to the requirement of a large amount of stack memory.…”

Section: Introductionmentioning

confidence: 99%

Statistical Learning Aided Decoding of BMST Tail-Biting Convolutional Code

Lin

Cai

et al. 2019

2019 IEEE International Symposium on Information Theory (ISIT)

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This paper is concerned with block Markov superposition transmission (BMST) of tail-biting convolutional code (TBCC). We propose a new decoding algorithm for BMST-TBCC, which integrates a serial list Viterbi algorithm (SLVA) with a soft check instead of conventional cyclic redundancy check (CRC). The basic idea is that, compared with an erroneous candidate codeword, the correct candidate codeword for the first sub-frame has less influence on the output of Viterbi algorithm for the second sub-frame. The threshold is then determined by statistical learning based on the introduced empirical divergence function. The numerical results illustrate that, under the constraint of equivalent decoding delay, the BMST-TBCC has comparable performance with the polar codes. As a result, BMST-TBCCs may find applications in the scenarios of the streaming ultra-reliable and low latency communication (URLLC) data services.Index Terms-Block Markov superposition transmission (BMST), list decoding, statistical learning, ultra-reliable and low latency communication (URLLC).

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“…In [5], a new kind of convolutional code, referred to as semi-random block-oriented convolutional code (SRBO-CC), was proposed for URLLC, which performs well with sequential decoding. However, the memory load of sequential decoding is heavy due to the requirement of a large amount of stack memory.…”

mentioning

confidence: 99%

List decoding with statistical check for semi‐random block‐oriented convolutional code

Lin

Wei

2019

Electron. lett.

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In this Letter, a list decoding algorithm is proposed for the semi-random block-oriented convolutional code (SRBO-CC). To decode each sub-frame, a list of candidates are serially computed until finding a qualified one or achieving maximum list, where the correctness of the decoding candidate is checked by a statistical threshold. Simulation results show that SRBO-CC with list decoding is competitive with polar codes and that the performance-complexity tradeoffs can be achieved by adjusting the statistical threshold.

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A Low Latency Coding Scheme: Semi-Random Block Oriented Convolutional Code

Cited by 7 publications

References 13 publications

Successive Cancellation List Decoding of Semi-random Unit Memory Convolutional Codes

Successive Cancellation List Decoding of Semi-random Unit Memory Convolutional Codes

Statistical Learning Aided Decoding of BMST Tail-Biting Convolutional Code

List decoding with statistical check for semi‐random block‐oriented convolutional code

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