Efficient Computation of Trellis Code Generating Functions

Shi, Jun; Wesel, Richard D.

doi:10.1109/tcomm.2003.822702

Cited by 31 publications

(23 citation statements)

References 15 publications

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“…As the constraint length of the practical convolutional encoder is usually larger than 4, such as the convolutional encoder in the IS-95 forward link has a constraint length of 9 [1], it is expected that the proposed technique can further outperform the state reduction algorithm. In addition, this proposed elimination order can also be applied in the second stage of the iterative finite state machine (FSM)-based approach proposed by Shi and Wesel [8] for the efficient computation of generating functions for trellis codes.…”

Section: Discussionmentioning

confidence: 99%

A modified state reduction algorithm for computing weight enumerators for convolutional codes

Mow

2005

Proceedings. International Symposium on Information Theory, 2005. ISIT 2005.

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The weight enumerator of a convolutional code is an important function that characterizes the codeword distance distribution and allows the error probability bounds of the code to be conveniently computed. An efficient state reduction algorithm to compute weight enumerators by iteratively modifying the symbolic adjacency matrix associated with the code was recently introduced. In this paper, we propose a dynamic elimination ordering technique that exploits the state diagram structure of the convolutional encoder for improving the efficiency of the state reduction algorithm.

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Section: Discussionmentioning

confidence: 99%

A modified state reduction algorithm for computing weight enumerators for convolutional codes

Mow

2005

Proceedings. International Symposium on Information Theory, 2005. ISIT 2005.

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“…where the summation in (1) is over all the possible n parallel transitions and I is a dummy variable which disappears before the derived upper bound expression. Pr(u →ū|u) is the conditional probability of a transition from state u to stateū given state u, and W(i → j) denotes the Hamming weight of the information sequence for the transition from i to j, where i ∈ {u, v} and j ∈ {ū,v} [20]. Herein,…”

Section: A Error Bound Over Irregular Constellation Casesmentioning

confidence: 99%

Convolutionally Coded SNR-Adaptive Transmission for Low-Latency Communications

Ilter

Yanıkömeroğlu

2018

IEEE Trans. Veh. Technol.

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Fifth generation new radio aims to facilitate new use cases in wireless communications. Some of these new use cases have highly demanding latency requirements; many of the powerful forward error correction codes deployed in current systems, such as the turbo and low-density parity-check codes, do not perform well when the low-latency requirement does not allow iterative decoding. As such, there is a rejuvenated interest in noniterative/one-shot decoding algorithms. Motivated by this, we propose a signal-to-noise ratio-adaptive convolutionally coded system with optimized constellations designed specifically for a particular set of convolutional code parameters. Numerical results show that significant performance improvements in terms of bit-error-rate and spectral efficiency can be obtained compared to the traditional adaptive modulation and coding systems in low-latency communications.

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“…The proof of the above Theorem is given in Appendix A. The transfer function Ψ(j, κ, η, ǫ, ζ) can be obtained by using a product state diagram [22,23]. A product state at time j is defined as (σ j ,σ j ), where σ j is a state of the encoder of a coded CPM system andσ j represents a state of the decoder.…”

Section: Rate Adaptive Coded Cpm Using Prccmentioning

confidence: 99%

Rate Flexible Coded Digital Phase Modulation (DPM) over Rings

Liu¹,

Lin

Peng

et al. 2014

2014 8th International Conference on Future Generation Communication and Networking

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In this paper, rate adaptive coded Digital Phase Modulation (DPM) schemes based on punctured ring convolutional codes are presented. We first present an upper bound on symbol error probability for punctured convolutional coded Continuous Phase Modulation (CPM) over rings with Maximum Likelihood Sequence Detection (MLSD). The bound is based on the transfer function technique, which is modified and generalized to punctured convolutional codes over rings. The novelty of this paper is the development of the analytical upper bound on the symbol error probability for the investigated system. This work provides a systematic way to analyze and design the rate adaptive ring convolutional coded CPM systems. The analysis method is very general. It may be applied to any trellis based coding schemes.

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Efficient Computation of Trellis Code Generating Functions

Cited by 31 publications

References 15 publications

A modified state reduction algorithm for computing weight enumerators for convolutional codes

A modified state reduction algorithm for computing weight enumerators for convolutional codes

Convolutionally Coded SNR-Adaptive Transmission for Low-Latency Communications

Rate Flexible Coded Digital Phase Modulation (DPM) over Rings

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