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

Garrido, Pablo; Sorensen, Chres W.; Lucani, Daniel E.; Agüero, Ramón

doi:10.1109/pimrc.2016.7794915

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…There have been many other sparse variants of random linear network coding, including chunked codes (e.g., [ 28 , 29 ]), tunable sparse network coding (e.g., [ 30 , 31 ], and sliding-window coding (e.g., [ 32 , 33 , 34 , 35 , 36 ]). While many of these codes can also be applied, BATS codes are more suitable for this distributed computing scenario.…”

Section: Bats-code-based Approachmentioning

confidence: 99%

Network Coding Approaches for Distributed Computation over Lossy Wireless Networks

Fan

Tang

et al. 2023

Entropy

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In wireless distributed computing systems, worker nodes connect to a master node wirelessly and perform large-scale computational tasks that are parallelized across them. However, the common phenomenon of straggling (i.e., worker nodes often experience unpredictable slowdown during computation and communication) and packet losses due to severe channel fading can significantly increase the latency of computational tasks. In this paper, we consider a heterogeneous, wireless, distributed computing system performing large-scale matrix multiplications which form the core of many machine learning applications. To address the aforementioned challenges, we first propose a random linear network coding (RLNC) approach that leverages the linearity of matrix multiplication, which has many salient properties, including ratelessness, maximum straggler tolerance and near-ideal load balancing. We then theoretically demonstrate that its latency converges to the optimum in probability when the matrix size grows to infinity. To combat the high encoding and decoding overheads of the RLNC approach, we further propose a practical variation based on batched sparse (BATS) code. The effectiveness of our proposed approaches is demonstrated by numerical simulations.

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Section: Bats-code-based Approachmentioning

confidence: 99%

Network Coding Approaches for Distributed Computation over Lossy Wireless Networks

Fan

Tang

et al. 2023

Entropy

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“…Since the decoding process has complexity of order O(N 3 ), density of encoding packets should be controlled such that optimal decoding complexity was to be maintained depending upon the network resources available. Garrido et al [39] introduced different approaches to control the density of encoding packets depending upon the feedback information from destination nodes at encoding nodes and they evaluated reduced complexity of 3.5 times without degradation of network performances. In [40], authors proposed a generation-based network coding (GNC) with an overhead-optimized decoder that combines precoding rate and random overlapping generations to obtain low decoding costs.…”

Section: Literature Reviewmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Amiri¹,

Dong²,

Pokhrel³

et al. 2019

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Optimal random network coding is reduced complexity in computation of coding coefficients, computation of encoded packets and coefficients are such that minimal transmission bandwidth is enough to transmit coding coefficient to the destinations and decoding process can be carried out as soon as encoded packets are started being received at the destination and decoding process has lower computational complexity. But in traditional random network coding, decoding process is possible only after receiving all encoded packets at receiving nodes. Optimal random network coding also reduces the cost of computation. In this research work, coding coefficient matrix size is determined by the size of layers which defines the number of symbols or packets being involved in coding process. Coding coefficient matrix elements are defined such that it has minimal operations of addition and multiplication during coding and decoding process reducing computational complexity by introducing sparseness in coding coefficients and partial decoding is also possible with the given coding coefficient matrix with systematic sparseness in coding coefficients resulting lower triangular coding coefficients matrix. For the optimal utility of computational resources, depending upon the computational resources unoccupied such as memory available resources budget tuned windowing size is used to define the size of the coefficient matrix. Keywords-Coding coefficients; computational complexity; lower triangular matrix; random network coding; sparse coding coefficients I. INTRODUCTIONITH the increase in the user's high data demand, the scientists and researchers around the globe are working together to achieve a higher data rate. The third generation 3GPP are planning to launch 5G standards until 2020.

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“…This, in programming implementation, still imposes high memory requirement for efficient random access of sparse matrix elements [ 21 ], otherwise the pivoting speed is significantly sacrificed. In practice, even for a moderate

as a few hundreds, the decoding speed of sparse GE can be unsatisfactory [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

An Irregular Graph Based Network Code for Low-Latency Content Distribution

Yang

2020

Sensors

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To fulfill the increasing demand on low-latency content distribution, this paper considers content distribution using generation-based network coding with the belief propagation decoder. We propose a framework to design generation-based network codes via characterizing them as building an irregular graph, and design the code by evaluating the graph. The and-or tree evaluation technique is extended to analyze the decoding performance. By allowing for non-constant generation sizes, we formulate optimization problems based on the analysis to design degree distributions from which generation sizes are drawn. Extensive simulation results show that the design may achieve both low decoding cost and transmission overhead as compared to existing schemes using constant generation sizes, and satisfactory decoding speed can be achieved. The scheme would be of interest to scenarios where (1) the network topology is not known, dynamically changing, and/or has cycles due to cooperation between end users, and (2) computational/memory costs of nodes are of concern but network transmission rate is spare.

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

Cited by 10 publications

References 18 publications

Network Coding Approaches for Distributed Computation over Lossy Wireless Networks

Network Coding Approaches for Distributed Computation over Lossy Wireless Networks

International Journal of Electronics and Telecommunications

An Irregular Graph Based Network Code for Low-Latency Content Distribution

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