Efficient error-correcting codes in the short blocklength regime

Coşkun, Mustafa Cemil; Durisi, Giuseppe; Jerkovits, Thomas; Liva, Gianluigi; Ryan, W.E.; Stein, Brian; Steiner, Fabian

doi:10.1016/j.phycom.2019.03.004

Cited by 124 publications

(95 citation statements)

References 112 publications

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“…We next apply Proposition 1 and Corollary 2 to the RCU s bound (8) and MC bound (11) to obtain their saddlepoint expansions. To this end, we first express the MGF and the CGF in terms of the generalized information density (6) and discuss their regions of convergence.…”

Section: Saddlepoint Expansions Of Rcu S and MC Boundsmentioning

confidence: 99%

“…In Fig. 7 we also show the performance of an accumulate-repeat-jagged-accumulate (ARJA) low-density parity-check (LDPC) code combined with 64-APSK modulation, pilot-assisted channel estimation (2 pilot symbols per coherence block), and maximum likelihood channel estimation followed by mismatched nearest-neighbor decoding at the receiver ("ARJA LDPC code 64-APSK"); for details see [6,Sec. 4].…”

Section: Numerical Examplesmentioning

confidence: 99%

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Saddlepoint Approximations for Short-Packet Wireless Communications

Lancho

Östman

Durisi

et al. 2020

IEEE Trans. Wireless Commun.

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In recent years, the derivation of nonasymptotic converse and achievability bounds on the maximum coding rate as a function of the error probability and blocklength has gained attention in the information theory literature. While these bounds are accurate for many scenarios of interest, they need to be evaluated numerically for most wireless channels of practical interest, and their evaluation is computationally demanding. This paper presents saddlepoint approximations of state-of-the-art converse and achievability bounds for noncoherent, single-antenna, Rayleigh block-fading channels. These approximations can be calculated efficiently and are shown to be accurate for SNR values as small as 0 dB and blocklengths of 168 channel uses or more. A. Lancho has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement number 714161) and from the Wallenberg AI, autonomous systems and software program. J. Östman and G. Durisi have been supported by the Swedish Research Council under grants 2014-6066 and 2016-03293. T. Koch has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement number 714161) and from the Spanish Ministerio de Economía y Competitividad under grants RYC-2014-16332 and TEC2016-78434-C3-3-R (AEI/FEDER, EU). G. Vazquez-Vilar

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Section: Saddlepoint Expansions Of Rcu S and MC Boundsmentioning

confidence: 99%

Section: Numerical Examplesmentioning

confidence: 99%

Saddlepoint Approximations for Short-Packet Wireless Communications

Lancho

Östman

Durisi

et al. 2020

IEEE Trans. Wireless Commun.

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“…All considered LDPC codes in this section are simulated over the binary-input AWGN (bi-AWGN) channel. All reference LDPC codes taken from [4] were designed through a girth optimization technique based on the PEG algorithm and are considered state-of-the-art: the standardized LDPC code by the Consultative Committee for Space Data Systems (CCSDS) for satellite telecommand links ( ), an accumulate-repeat-3-accumulate (AR3A) LDPC code ( ), an accumulate-repeat-jagged-accumulate (ARJA) LDPC code ( ), and the proposed protograph-based LDPC code for the upcoming 5G NR standard with a base graph (base graph 2 in [20]) optimized for small blocklengths ( ). As a calibration step of our decoding framework, we were able to reproduce exactly the same BLER curves using our own simulation setup.…”

Section: Genetic Algorithm-based Ldpc Code Designmentioning

confidence: 99%

“…BLER Construction @ design SNR Construction @ design SNR CCSDS Up-Link LDPC [21] (3,6) Regular LDPC AR3A LDPC from [4] ARJA LDPC from [4] 5G LDPC [20] GenAlg LDPC @ 5 dB GenAlg IRA @ 5 dB…”

Section: A Error-rate Performancementioning

confidence: 99%

“…All codes decoded with N it,max = 20 except ( ) AR3A LDPC from[4]; N it,max = 200 Average decoding complexity; E is the number of edges in the graph of the code Several (n = 128, k = 64) LDPC codes decoded with BP decoding using N it,max = 20 iterations over the bi-AWGN channel. Evolution of the BLER at design SNR E b /N 0 = 5 dB; (n = 128, k = 64) LDPC codes; BP decoding with N it,max = 20; bi-AWGN channel.…”

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Decoder-in-the-Loop: Genetic Optimization-Based LDPC Code Design

et al. 2019

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LDPC code design tools typically rely on asymptotic code behavior and are affected by an unavoidable performance degradation due to model imperfections in the short length regime. We propose an LDPC code design scheme based on an evolutionary algorithm, the Genetic Algorithm (GenAlg), implementing a "decoder-in-the-loop" concept. It inherently takes into consideration the channel, code length and the number of iterations while optimizing the error-rate of the actual decoder hardware architecture. We construct short length LDPC codes (i.e., the parity-check matrix) with error-rate performance comparable to, or even outperforming that of well-designed standardized short length LDPC codes over both AWGN and Rayleigh fading channels. Our proposed algorithm can be used to design LDPC codes with special graph structures (e.g., accumulatorbased codes) to facilitate the encoding step, or to satisfy any other practical requirement. Moreover, GenAlg can be used to design LDPC codes with the aim of reducing decoding latency and complexity, leading to coding gains of up to 0.325 dB and 0.8 dB at BLER of 10 −5 for both AWGN and Rayleigh fading channels, respectively, when compared to state-of-the-art short LDPC codes. Also, we analyze what can be learned from the resulting codes and, as such, the GenAlg particularly highlights design paradigms of short length LDPC codes (e.g., codes with degree-1 variable nodes obtain very good results).

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Advanced channel coding schemes for B5G/6G networks: State‐of‐the‐art analysis, research challenges and future directions

Gautam,

Thakur,

Singh

2024

Int J Communication

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SummaryAs a consequence of continuing demand for additional capacity and higher quality data transmission, channel coding as a key component of a digital communication network has progressed from a classical single pair, point‐to‐point information theoretic application to a class of coding techniques with diverse parameters. The heterogeneous requirements of B5G/6G networks technology have made flexibility of code parameters the main desired feature while designing channel codes. Owing to this, channel coding techniques have evolved to support enabling applications that depend on different factors such as latency, reliability, energy efficiency and throughput. We review and discuss the application of new techniques, modifications in the design and construction of modern channel coding schemes, including block, convolutional, rateless and space‐time codes. Further, the error correction performance of mainstream channel decoders for LDPC, polar and turbo codes is compared and analysed for their attainable error rate. The aim of this study is to highlight the adaptability of the discussed coding schemes for the forthcoming cellular network landscape. This article also addresses the implementational challenges of high throughput, low latency and machine type communication scenarios. Moreover, some recent studies exploring the role of machine learning for optimisation of B5G/6G networks are classified and discussed. Concluding, research areas pertaining to the scalability of error control coding for next generation networking are addressed.

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Efficient error-correcting codes in the short blocklength regime

Cited by 124 publications

References 112 publications

Saddlepoint Approximations for Short-Packet Wireless Communications

Saddlepoint Approximations for Short-Packet Wireless Communications

Decoder-in-the-Loop: Genetic Optimization-Based LDPC Code Design

Advanced channel coding schemes for B5G/6G networks: State‐of‐the‐art analysis, research challenges and future directions

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