Systematic network coding for two-hop lossy transmissions

Li, Ye; Blostein, Steven D.; Chan, Wai-Yip

doi:10.1186/s13634-015-0273-3

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…Obviously, the issue of rank deficiency can be solved since the receiver can still decode some original packets even though the receiver cannot receive a sufficient number of linearly independent packets. On the other hand, SNC can achieve the same performance as the receiver receives a sufficient number of transmitted packets [ 22 , 23 ]. In addition, the second drawback (i.e., high decoding complexity) can be solved by brief propagation (BP) and Gaussian Elimination (GE) algorithms that are used for decoding with low computational complexity compared to RLNC [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

“…In this subsection, we compared the performance of our scheme to two other baseline schemes that were RLNC [ 16 ] and SNC [ 23 ]. This is because both of them employed redundancy-based reliability mechanism to use the advantage of coding for providing the capability of error correction over lossy wireless channel like our proposal scheme.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Distributed Systematic Network Coding for Reliable Content Uploading in Wireless Multimedia Sensor Networks

Chau

Shin

Jeong

2018

Sensors

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Recently, the wireless sensor network paradigm is shifting toward research aimed at enabling the robust delivery of multimedia content. A challenge is to deliver multimedia content with predefined levels of Quality of Service (QoS) under resource constraints such as bandwidth, energy, and delay. In this paper, we propose a distributed systematic network coding (DSNC) scheme for reliable multimedia content uploading over wireless multimedia sensor networks, in which a large number of multimedia sensor nodes upload their own content to a sink through a cluster head node. The design objective is to increase the reliability and bandwidth-efficient utilization in uploading with low decoding complexity. The proposed scheme consists of two phases: in the first phase, each sensor node distributedly encodes the content into systematic network coding packets and transmits them to the cluster head; then in the second phase, the cluster head encodes all successfully decoded incoming packets from multiple sensor nodes into innovative systematic network coding packets and transmits them to the sink. A bandwidth-efficient and channel-aware error control algorithm is proposed to enhance the bandwidth-efficient utilization by dynamically determining the optimal number of innovative coded packets. For performance analysis and evaluation, we firstly derive the closed-form equations of decoding probability to validate the effectiveness of the proposed uploading scheme. Furthermore, we perform various simulations along with a discussion in terms of three performance metrics: decoding probability, redundancy, and image quality measurement. The analytical and experimental results demonstrate that the performance of our proposed DSNC outperforms the existing uploading schemes.

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Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Distributed Systematic Network Coding for Reliable Content Uploading in Wireless Multimedia Sensor Networks

Chau

Shin

Jeong

2018

Sensors

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“…These studies investigated the tradeoffs between AL-FEC, physical, and MAC layer parameters for broadcast services over WLANs [12,13], Long-Term Evolution (LTE) [14,15], and Digital Video Broadcasting-Handheld (DVB-H) [16] and found that only a welldesigned and optimized system can maximize spectral efficiency and user's QoE. Some research has been conducted into cross-layer optimization frameworks that modify AL-FEC redundancy based on the physical layer modulation and coding schemes (MCSs) used to enable dependable video streaming applications across unreliable wireless channels [17][18][19][20][21]. However, because each multicast user has a different channel condition, multicast video broadcasting to numerous viewers poses extra limitations.…”

Section: Introductionmentioning

confidence: 99%

Application Layer-Forward Error Correction Raptor Q Codes in 5G Mobile Networks for Factory of the Future

Ramly

Nordin

Abdullah

2022

Wireless Communications and Mobile Computing

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The future communication requirements for industrial automation will differ significantly from existing technologies. Traffic in Industry 4.0 imposes real-time requirements, requiring ultra-reliable communication (URC) with high reliability and minimal latency. The demand for ultra-high reliability as high as 99.999999 percent and as low as 1 ms end-to-end latency is the major challenge of the NOMA communication system in the factory of the future. The high expectations on reliability and latency need modifications to the radio system’s baseband signal processing, medium access control (MAC) layer, and application layer to protect against packet losses. Thus, this paper investigates the utilization of Raptor Q codes, which is a type of Application Layer Forward Error Correction (AL-FEC), by developing an end-to-end system level simulator to evaluate and analyze the performance of the transmission signals based on cross-layer approach; from the physical layer (PHY), MAC layer, and application layer parameters in an indoor factory network setting. The factory is assumed to be operated with various factory robots of different speeds, from static to 10 km/h. The 5G technology relies heavily on flexible network operations. High user density, high user mobility, deployment, and coverage are all qualities that allow for this flexibility. Through extensive simulations, the results showed that the Raptor Q codes are not only able to give good results, i.e., packet reception rate PRR = 0.9 of 10 m or 1.8% to 10 m or 3.4% depending on different scenarios, but are also able to meet PRR = 0.9 in the mobility scenario at 10 km/h. Thus, the Raptor Q codes can be seen as a good candidate for obtaining results within a strict range of requirements set by URC communications for the factory of the future replacing RLNC.

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“…In addition, the authors showed that decoding complexity and per‐packet delay can be reduced if packets can traverse the entire network in the systematic (uncoded) form. Hence, SNC has been exploited in many applications such as content distribution [2], sensor networks or Wi‐Fi offloading [3], and multimedia streaming [4]. Nevertheless, the complete analysis of the decoding performance has not been fully derived.…”

Section: Introductionmentioning

confidence: 99%

Analysis of decoding probability for systematic wireless network coding

et al. 2017

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Systematic wireless network coding has been proposed as a potential solution to solve the major drawback of high encoding/decoding complexities of random linear network coding and to increase the decoding capability. However, the complete analysis of the decoding performance has not been fully derived. In this Letter, the authors derive closed-form expressions for the decoding probability under systematic network coding (SNC) over single-hop and multi-hop wireless networks. They validate the analysis by comparing simulation results. Their formulation provides a theoretical analysis for further understanding of the SNC and can be used to improve the current upper bound for the decoding probability.

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Systematic network coding for two-hop lossy transmissions

Cited by 10 publications

References 22 publications

Distributed Systematic Network Coding for Reliable Content Uploading in Wireless Multimedia Sensor Networks

Distributed Systematic Network Coding for Reliable Content Uploading in Wireless Multimedia Sensor Networks

Application Layer-Forward Error Correction Raptor Q Codes in 5G Mobile Networks for Factory of the Future

Analysis of decoding probability for systematic wireless network coding

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