Caterpillar RLNC With Feedback (CRLNC-FB): Reducing Delay in Selective Repeat ARQ Through Coding

Gabriel, Frank; Wunderlich, Simon; Pandi, Sreekrishna; Fitzek, Frank H. P.; Reisslein, Martin

doi:10.1109/access.2018.2865137

Cited by 41 publications

(14 citation statements)

References 84 publications

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“…The present study has focused on generation based RLNC. It would be interesting in future research to explore the DSEP concept in the context of sliding window RLNC [42]- [47]. For sliding window RLNC, a DSEP policy could adapt the sparsity and extra coded packets according to the status of the receiver for a given current sliding window position.…”

Section: Discussionmentioning

confidence: 99%

“…RLNC emerged from the compute-and-forward paradigm [38]- [41], which recodes packets in intermediate network nodes. RLNC can operate on blocks of source packets or a sliding window covering multiple source packets [42]- [47]. We develop DSEP Fulcrum as a block code that builds on RLNC block coding.…”

Section: B Related Workmentioning

confidence: 99%

“…Sparse RLNC [73]- [78] has been examined in a range of contexts, including broadcast systems [79]- [81], data compression [82], and secure communications [83]. The decoding probability and delay for sparse RLNC have been analyzed in [84]- [88], while the tuning of the sparsity has been examined in [89]- [92], and sparsity for sliding window RLNC [42]- [47] has been studied in [93], [94]. To the best of our knowledge, sparse RLNC has not yet been examined in the context of the flexible Fulcrum RLNC approach.…”

Section: B Related Workmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

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

et al. 2020

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“…The batch-based approach has been considered for a wide range of communication scenarios, such as delay-tolerant networks in [53], video delivery networks in [54,55], industrial networks in [56,57], and some complex sensor networks in [58]. Recent studies have adapted the batch positions and sizes to conform to different sliding window policies in an effort to reduce the RLNC communication protocol delay [59][60][61][62][63][64].…”

Section: Mathematics Of Rlncmentioning

confidence: 99%

Hardware Acceleration for RLNC: A Case Study Based on the Xtensa Processor with the Tensilica Instruction-Set Extension

et al. 2018

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Random linear network coding (RLNC) can greatly aid data transmission in lossy wireless networks. However, RLNC requires computationally complex matrix multiplications and inversions in finite fields (Galois fields). These computations are highly demanding for energy-constrained mobile devices. The presented case study evaluates hardware acceleration strategies for RLNC in the context of the Tensilica Xtensa LX5 processor with the tensilica instruction set extension (TIE). More specifically, we develop TIEs for multiply-accumulate (MAC) operations for accelerating matrix multiplications in Galois fields, single instruction multiple data (SIMD) instructions operating on consecutive memory locations, as well as the flexible-length instruction extension (FLIX). We evaluate the number of clock cycles required for RLNC encoding and decoding without and with the MAC, SIMD, and FLIX acceleration strategies. We also evaluate the RLNC encoding and decoding throughput and energy consumption for a range of RLNC generation and code word sizes. We find that for GF ( 2 8 ) and GF ( 2 16 ) RLNC encoding, the SIMD and FLIX acceleration strategies achieve speedups of approximately four hundred fold compared to a benchmark C code implementation without TIE. We also find that the unicore Xtensa LX5 with SIMD has seven to thirty times higher RLNC encoding and decoding throughput than the state-of-the-art ODROID XU3 system-on-a-chip (SoC) operating with a single core; the Xtensa LX5 with FLIX, in turn, increases the throughput by roughly 25% compared to utilizing only SIMD. Furthermore, the Xtensa LX5 with FLIX consumes roughly four orders of magnitude less energy than the ODROID XU3 SoC.

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“…By overlapping generations, an encoding sliding window is defined, which covers the source symbols belonging to the newest generation. An example of SysNC with finite sliding encoding and decoding windows is the Caterpillar RLNC (CRLNC) protocol, presented in [11] and extended with ARQ (Automatic Repeat reQuest) functionality in [12]. CRLNC offers decoding probability very close to the block based systematic RLNC, but with much lower delay.…”

Section: Introductionmentioning

confidence: 99%

Systematic Network Coding with Overlap for IoT Scenarios

Zverev

Garrido

Agüero

et al. 2019

2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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The presence of IoT in current networking scenarios is more relevant every day. IoT covers a wide range of applications, ranging from wearable devices to vehicular communications. With the consolidation of Industry 4.0, IIoT (Industrial IoT) environments are becoming more common. Communications in these scenarios are mostly wireless, and due to the lossy nature of wireless communications, the loss of information becomes an intrinsic problem. However, loss recovery schemes increase the delay that characterizes any communication. On the other hand, both reliability (robustness) and low delay are crucial requirements for some applications in IIoT. An interesting strategy to improve both of them is the use of Network Coding techniques, which have shown promising results, in terms of increasing reliability and performance. This work focuses on a possible new coding approach, based on systematic network coding scheme with overlapping generations. We perform a thorough analysis of its behavior. Based on the results, we draw out a number of conclusions for practical implementations in wireless networks, focusing our interest in IIoT environments.

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Caterpillar RLNC With Feedback (CRLNC-FB): Reducing Delay in Selective Repeat ARQ Through Coding

Cited by 41 publications

References 84 publications

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

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

Hardware Acceleration for RLNC: A Case Study Based on the Xtensa Processor with the Tensilica Instruction-Set Extension

Systematic Network Coding with Overlap for IoT Scenarios

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