Leaner and meaner: Network coding in SIMD enabled commercial devices

Sorensen, Chres W.; Paramanathan, Achuthan; Cabrera, Juan A.; Pedersen, Morten Videbæk; Lucani, Daniel E.; Fitzek, Frank H. P.

doi:10.1109/wcnc.2016.7565066

Cited by 14 publications

(11 citation statements)

References 16 publications

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“…The decoding throughput is defined as the amount of payload data in a generation, i.e., n× data packet size, divided by the decoding computation time for recovering the n original data packets. Generally, the energy consumption of RLNC encoding and decoding is linearly proportional to the computational complexity; the encoding and decoding throughput in turn is inversely proportional to the computational complexity [98], [99]. Thus, a high throughput indicates low energy consumption and vice versa.…”

Section: Evaluation 1) Evaluation Setupmentioning

confidence: 99%

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

et al. 2020

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Fulcrum coding combines a high-field outer Random Linear Network Coding (RLNC) that generates outer coding expansion packets with a small-field inner RLNC that combines the source packets and the outer coding expansion packets. This two-layer Fulcrum coding allows flexible decoding in receivers with heterogeneous computational capabilities. Fulcrum coding has so far only been studied for conventional dense RLNC, which randomly selects all coding coefficients, and only for a statically fixed number of outer expansion packets. However, the probability that the coding coefficient row of a newly received packet is linearly independent of prior received coding coefficient rows (a prerequisite for successful decoding) is highly dynamic. We propose to exploit the dynamics of this probability to reduce the computational complexity of Fulcrum coding. In particular, we vary the density of non-zero coding coefficients, i.e., equivalently, the sparsity of coding coefficients, and the number of outer expansion packets to keep the complexity low while maintaining a reasonably high decoding probability. We introduce the general principles of dynamic sparsity and expansion packets (DSEP) for Fulcrum coding as well as two specific example DSEP policies. Our evaluations indicate that DSEP Fulcrum can increase the encoding throughput tenfold and increase the decoding throughput 1.4 to 4.3 fold while achieving decoding probabilities that are typically less than 1% lower than the conventional Fulcrum decoding probabilities. We also find that DSEP achieves somewhat higher encoding and decoding throughputs than the CodornicesRq (Release 2.1) implementation of RaptorQ block coding for small blocks (generations) of source packets, while RaptorQ is substantially faster for large generation sizes. Furthermore, we develop and evaluate an elementary DSEP recoding mechanism that achieves a recoding throughput more than double the decoding throughput.

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Section: Evaluation 1) Evaluation Setupmentioning

confidence: 99%

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

et al. 2020

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“…These results were presented in [18], where they analyzed the energy consumed by different devices (Raspberry, Raspberry v2, Samsung Galaxy 3, Samsung Galaxy 5), leading to an overall average reduction of around 2.5.…”

Section: A System Descriptionmentioning

confidence: 99%

Performance and complexity of tunable sparse network coding with gradual growing tuning functions over wireless networks

Garrido

Sorensen

Lucani

et al. 2016

2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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Abstract-Random Linear Network Coding (RLNC) has been shown to be a technique with several benefits, in particular when applied over wireless mesh networks, since it provides robustness against packet losses. On the other hand, Tunable Sparse Network Coding (TSNC) is a promising concept, which leverages a trade-off between computational complexity and goodput. An optimal density tuning function has not been found yet, due to the lack of a closed-form expression that links density, performance and computational cost. In addition, it would be difficult to implement, due to the feedback delay. In this work we propose two novel tuning functions with a lower computational cost, which do not highly increase the overhead in terms of the transmission of linear dependent packets compared with RLNC and previous proposals. Furthermore, we also broaden previous studies of TSNC techniques, by means of an extensive simulation campaign carried out using the ns-3 simulator. This brings the possibility of assessing their performance over more realistic scenarios, e.g considering MAC effects and delays. We exploit this implementation to analyze the impact of the feedback sent by the decoder. The results, compared to RLNC, show a reduction of 3.5 times in the number of operations without jeopardizing the network performance, in terms of goodput, even when we consider the delay effect on the feedback sent by the decoder.

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“…Throughout this section we select the generation size to be g = 64. The latter is one of the values that provide the highest benefits in NC [81,91]; hence it is commonly used in the literature. Other common alternative is g = 32.…”

Section: Resultsmentioning

confidence: 99%

“…Please recall the subframe duration in LTE-A is t s = 1 ms. A typical UDP data packet of length = 1470 bytes is used. Up to one data packet is transmitted to each UE in the MC per subframe, which [91], where a Samsung Galaxy S5 was considered. Other parameter settings are listed in Table 6.3.…”

Section: Resultsmentioning

confidence: 99%

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On reliable and energy efficient massive wireless communications: the road to 5G

Leyva-Mayorga¹

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To my wife, Mónica, and to my parents, because they have given me the most precious gifts I can think of: their love, confidence, and support. v Lastly, I would like to thank the support provided by the Consejo Nacional de Ciencia y Tecnología, México (CONACYT) and by the Consejo Mexiquense de Ciencia y Tecnología (COMECYT) through grant CONACYT-GEM 383936 for postgraduate studies in a foreign country. This includes the support for my short stays at the Universidad Autónoma del Estado de México (UAEM). These stays were only possible due to the support provided by Prof. Otniel Portillo Rodríguez. Thanks for the invitation.

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Leaner and meaner: Network coding in SIMD enabled commercial devices

Cited by 14 publications

References 16 publications

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

Performance and complexity of tunable sparse network coding with gradual growing tuning functions over wireless networks

On reliable and energy efficient massive wireless communications: the road to 5G

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