Delay-Complexity Trade-Off of Random Linear Network Coding in Wireless Broadcast

Su, Rina; Sun, Qifu Tyler; Zhang, Zhongshan

doi:10.1109/tcomm.2020.3001133

Cited by 19 publications

(6 citation statements)

References 21 publications

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“…Accordingly, it requires at most

multiplications and

additions over GF(

) in the decoding process. Following the same consideration in [ 4 , 18 , 19 ], we assume that it respectively takes

and

binary operations to realize addition and multiplication between two elements in GF(

). Consequently, the total number of required binary operations can be characterized as

to recover every M -bit original packet.…”

Section: Framework Descriptionmentioning

confidence: 99%

“…Herein, we did not consider the complexity to compute the inverse matrix of

because in practice the packet length M is much larger than n , and this convention has also been adopted in [ 4 , 19 ] for computational complexity analysis.…”

Section: Framework Descriptionmentioning

confidence: 99%

“…In this section, we numerically analyze the performance of applying the proposed systematic scalable RLNC scheme to a wireless broadcast network, which is a classical model to demonstrate the advantage of RLNC [ 4 , 5 , 6 , 7 ]. The number n of original packets in a batch is varied from

to 24.…”

Section: Numerical Analysismentioning

confidence: 99%

“…Random linear network coding (RLNC) provides a distributed and asymptotically optimal approach for linear coding with coefficients randomly selected from a base field [ 1 ]. It shows the potential to improve the performance of unreliable or topologically unknown networks such as D2D networks [ 2 ], ad hoc networks [ 3 ], and wireless broadcast networks [ 4 , 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Scalable Network Coding for Heterogeneous Devices over Embedded Fields

Tang

Zheng

et al. 2022

Entropy

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In complex network environments, there always exist heterogeneous devices with different computational powers. In this work, we propose a novel scalable random linear network coding (RLNC) framework based on embedded fields, so as to endow heterogeneous receivers with different decoding capabilities. In this framework, the source linearly combines the original packets over embedded fields based on a precoding matrix and then encodes the precoded packets over GF(2) before transmission to the network. After justifying the arithmetic compatibility over different finite fields in the encoding process, we derive a sufficient and necessary condition for decodability over different fields. Moreover, we theoretically study the construction of an optimal precoding matrix in terms of decodability. The numerical analysis in classical wireless broadcast networks illustrates that the proposed scalable RLNC not only guarantees a better decoding compatibility over different fields compared with classical RLNC over a single field, but also outperforms Fulcrum RLNC in terms of a better decoding performance over GF(2). Moreover, we take the sparsity of the received binary coding vector into consideration, and demonstrate that for a large enough batch size, this sparsity does not affect the completion delay performance much in a wireless broadcast network.

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“…Accordingly, it requires at most

multiplications and

additions over GF(

) in the decoding process. Following the same consideration in [ 4 , 18 , 19 ], we assume that it respectively takes

and

binary operations to realize addition and multiplication between two elements in GF(

). Consequently, the total number of required binary operations can be characterized as

to recover every M -bit original packet.…”

Section: Framework Descriptionmentioning

confidence: 99%

“…Herein, we did not consider the complexity to compute the inverse matrix of

because in practice the packet length M is much larger than n , and this convention has also been adopted in [ 4 , 19 ] for computational complexity analysis.…”

Section: Framework Descriptionmentioning

confidence: 99%

to 24.…”

Section: Numerical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Scalable Network Coding for Heterogeneous Devices over Embedded Fields

Tang

Zheng

et al. 2022

Entropy

Self Cite

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show abstract

“…1 The computational complexity becomes a critical issue for resource-limited edge devices, as NC often resorts to a significantly large amount of computation to the devices in mobile wireless networks (e.g., battery-powered devices). This is because mobile devices may suffer from significant computational complexity during the decoding process, even with only a few coded packets [23]. Furthermore, the computational complexity of NC in the decoding process can cause a throughput bottleneck in comparison to the achievable speed of a wireless local area network interface [24].…”

Section: Introductionmentioning

confidence: 99%

Data Dissemination Framework Using Low-Rank Approximation in Edge Networks

Kwon,

Park

2024

IEEE Access

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This paper proposes a reliable data dissemination framework for edge networks, leveraging network coding combined with low-rank approximation. We consider an edge network that consists of a server and power-limited mobile devices, where the data is broadcasted by the server. In such networks, broadcasted data may be lost due to poor channel conditions or the interference incurred by the mobility of edge mobile devices, particularly without a retransmission mechanism. This can cause application errors in edge devices, lower the Quality of Service (QoS), and compromise network stability. To overcome these challenges, we propose a framework for reliable edge networks in broadcasting without retransmissions. The edge network reliability can be achieved by the approximate decoding of broadcasted data. In the proposed framework, the edge server employs matrix factorization to encode data with principal components, ensuring a lower decoding error rate even with potential packet losses. Furthermore, the proposed framework can shift the computational complexity from mobile edge devices to the edge server based on the low-rank approximation at the decoding stage, effectively mitigating power limitations on mobile devices. Through theoretical analysis, we demonstrate that the proposed algorithm outperforms typical broadcasting in terms of decoding accuracy, and present an upper bound error rate for the proposed algorithm. The simulation results confirm that the proposed algorithm outperforms other state-of-the-art algorithms in terms of decoding accuracy, time delay, and complexity.

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Indoor intrusion detection based on deep signal feature fusion and minimized-MKMMD transfer learning

Zhou

Wang

et al. 2020

Physical Communication

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Delay-Complexity Trade-Off of Random Linear Network Coding in Wireless Broadcast

Cited by 19 publications

References 21 publications

Scalable Network Coding for Heterogeneous Devices over Embedded Fields

Scalable Network Coding for Heterogeneous Devices over Embedded Fields

Data Dissemination Framework Using Low-Rank Approximation in Edge Networks

Indoor intrusion detection based on deep signal feature fusion and minimized-MKMMD transfer learning

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