Polar Coding for Efficient Transport Layer Multicast

Pereira, Pablo Gil; Herfet, Thorsten

doi:10.1109/ccnc49033.2022.9700718

Cited by 3 publications

(8 citation statements)

References 21 publications

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“…With virtual interleaving the packets can be split into smaller symbols of m bits, k packets are grouped in the interleaver buffer, and the coding operations are iterated throughout the complete packet length. In [29] we showed that the packetization directly impacts the complexity of the system: while the matrix inversion has typically dominated the run-time complexity of coding in the physical layer, the matrix-vector multiplication dominates the packetized layers. As a result, a different code construction may be the best option depending on the channel conditions and platform the protocol runs on.…”

Section: Packet Codingmentioning

confidence: 96%

“…The end-to-end design paradigm [25] has led to many proposals to complement error coding in the lower layers with coding at the transport layer in order to improve reliability without prohibitively increasing the delay [11,12,21,22,[26][27][28][29][30]. Maximum Distance Separable (MDS) block codes ensure that the number of correctable losses equals the number of transmitted parity packets.…”

Section: Related Workmentioning

confidence: 99%

“…MDS codes have been used to provide predictable reliability under time constraints [21], reduce delay in multimedia communication [28], and avoid feedback implosion in multicast [16,31]. Despite their high loss rate floor, and hence a redundancy transmission overhead to achieve the same performance as MDS codes, binary codes have also been a mechanism of choice due to their reduced coding complexity [11,29,30]. Finally, making the end-to-end delay independent of the block length is possible with windowed Random Linear Codes (RLC), which evenly distribute the parity packets over the source packets.…”

Section: Related Workmentioning

confidence: 99%

“…RLC codes have proved to reduce the in-order delay, and hence the tail delay in fully reliable protocols [15,32]. However, this delay reduction in RLC codes comes at the cost of lower code rates than block codes [33], and the run-time complexity of their matrix inversion function hinders their deployment in packetized layers [29,34,35]. Michel et al [12] have extended QUIC with the three aforementioned code families, showing that RLC codes achieve the lowest delay.…”

Section: Related Workmentioning

confidence: 99%

“…Three different families of codes have been proposed for the transport layer: MDS [21,22,31], binary [11,29,30], and RLC codes [18,34,35]. They vary in error correction capabilities, underlying field size, and generator matrix construction, and they have different algorithmic tools at their disposal for efficient implementation [31,44].…”

Section: Code Constructionmentioning

confidence: 99%

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DeepSHARQ: Hybrid Error Coding using Deep Learning

Pereira¹,

Vogelgesang²,

Miodek³

et al. 2023

Preprint

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Purpose: Cyber-Physical Systems operate under changing environments and on resource-constrained devices. Communication in these environments must use hybrid error coding, as pure pro- or reactiveschemes cannot always fulfill application demands or have suboptimal performance. However, findingoptimal coding configurations that fulfill application constraints—e.g., tolerate loss and delay—underchanging channel conditions is a computationally challenging task. Methods: Recently, the systems community has started addressing these sorts of problems usinghybrid decomposed solutions, i.e., algorithmic approaches for well-understood formalized parts ofthe problem and learning-based approaches for parts that must be estimated (either for reasons ofuncertainty or computational intractability). For DeepSHARQ, we revisit our own recent work andlimit the learning problem to block length prediction, the major contributor to inference time (andits variation) when searching for hybrid error coding configurations. The remaining parameters arefound algorithmically, and hence we make individual contributions with respect to finding close-to-optimal coding configurations in both of these areas—combining them into a hybrid solution. Results: DeepSHARQ applies block length regularization in order to reduce the neural networksin comparison to purely learning-based solutions. The hybrid solution is nearly optimal concerningthe channel efficiency of coding configurations it generates, as it is trained so deviations from theoptimum are upper-bound by a configurable percentage. Additionally, DeepSHARQ is capable ofreacting to channel changes in real-time, thereby enabling Cyber-Physical Systems even on resource-constrained platforms. Conclusion: Tightly integrating algorithmic and learning-based approaches allows DeepSHARQ toreact to channel changes faster and with a more predictable time than solutions that rely only oneither of the two approaches.

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Section: Packet Codingmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Code Constructionmentioning

confidence: 99%

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DeepSHARQ: Hybrid Error Coding using Deep Learning

Pereira¹,

Vogelgesang²,

Miodek³

et al. 2023

Preprint

View full text Add to dashboard Cite

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DeepSHARQ: hybrid error coding using deep learning

Pereira

Vogelgesang

Miodek

et al. 2023

J Reliable Intell Environ

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Cyber-physical systems operate under changing environments and on resource-constrained devices. Communication in these environments must use hybrid error coding, as pure pro- or reactive schemes cannot always fulfill application demands or have suboptimal performance. However, finding optimal coding configurations that fulfill application constraints—e.g., tolerate loss and delay—under changing channel conditions is a computationally challenging task. Recently, the systems community has started addressing these sorts of problems using hybrid decomposed solutions, i.e., algorithmic approaches for well-understood formalized parts of the problem and learning-based approaches for parts that must be estimated (either for reasons of uncertainty or computational intractability). For DeepSHARQ, we revisit our own recent work and limit the learning problem to block length prediction, the major contributor to inference time (and its variation) when searching for hybrid error coding configurations. The remaining parameters are found algorithmically, and hence we make individual contributions with respect to finding close-to-optimal coding configurations in both of these areas—combining them into a hybrid solution. DeepSHARQ applies block length regularization in order to reduce the neural networks in comparison to purely learning-based solutions. The hybrid solution is nearly optimal concerning the channel efficiency of coding configurations it generates, as it is trained so deviations from the optimum are upper bound by a configurable percentage. In addition, DeepSHARQ is capable of reacting to channel changes in real time, thereby enabling cyber-physical systems even on resource-constrained platforms. Tightly integrating algorithmic and learning-based approaches allows DeepSHARQ to react to channel changes faster and with a more predictable time than solutions that rely only on either of the two approaches.

show abstract