On Vector Random Linear Network Coding in Wireless Broadcasts

Compared with scalar linear network coding (LNC) formulated over the finite field GF(2L), vector LNC offers enhanced flexibility in the code design by enabling linear operations over the vector space GF(2)L and demonstrates a number of advantages over scalar LNC. While random LNC (RLNC) has shown significant potential to improve the completion delay performance in wireless broadcasts, most prior studies focus on scalar RLNC. In particular, it is well known that, with increasing <i>L</i>, primitive scalar RLNC over GF(2L) asymptotically achieves the optimal completion delay. However, the completion delay performance of primitive vector RLNC remains unexplored. This work aims to fill in this blank. We derive closed-form expressions for the probability distribution and the expected value of both the completion delay at a single receiver and the system completion delay. We further unveil a fundamental limitation that is different from scalar RLNC: even for large enough <i>L</i>, primitive vector RLNC over GF(2)L inherently fails to reach optimal completion delay. In spite of this, the gap between the expected completion delay at a receiver and the optimal one is shown to be a constant smaller than 0.714, which implies that the expected completion delay normalized by the number <i>P</i> of original packets is asymptotically optimal with increasing <i>P</i>. We also validate our theoretical characterization through numerical simulations. Our theoretical characterization establishes primitive vector RLNC as a performance baseline for the future design of practical vector RLNC schemes with different design goals.

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Publication Details

Journal
Entropy
Published
2025-05-26
DOI
https://doi.org/10.3390/e27060559
Primary Topic
Cooperative Communication and Network Coding
Type
article
Field-Weighted Citation Impact
0.00

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article

On Vector Random Linear Network Coding in Wireless Broadcasts

Qifu Tyler Sun, Rina Su, Zhongshan Zhang, Chengji Zhao
Entropy
Cooperative Communication and Network Coding
article

On Vector Random Linear Network Coding in Wireless Broadcasts

Qifu Tyler Sun, Rina Su, Zhongshan Zhang, Chengji Zhao
article en

Abstract

Compared with scalar linear network coding (LNC) formulated over the finite field GF(2L), vector LNC offers enhanced flexibility in the code design by enabling linear operations over the vector space GF(2)L and demonstrates a number of advantages over scalar LNC. While random LNC (RLNC) has shown significant potential to improve the completion delay performance in wireless broadcasts, most prior studies focus on scalar RLNC. In particular, it is well known that, with increasing <i>L</i>, primitive scalar RLNC over GF(2L) asymptotically achieves the optimal completion delay. However, the completion delay performance of primitive vector RLNC remains unexplored. This work aims to fill in this blank. We derive closed-form expressions for the probability distribution and the expected value of both the completion delay at a single receiver and the system completion delay. We further unveil a fundamental limitation that is different from scalar RLNC: even for large enough <i>L</i>, primitive vector RLNC over GF(2)L inherently fails to reach optimal completion delay. In spite of this, the gap between the expected completion delay at a receiver and the optimal one is shown to be a constant smaller than 0.714, which implies that the expected completion delay normalized by the number <i>P</i> of original packets is asymptotically optimal with increasing <i>P</i>. We also validate our theoretical characterization through numerical simulations. Our theoretical characterization establishes primitive vector RLNC as a performance baseline for the future design of practical vector RLNC schemes with different design goals.

EntropyVol. 27(6)
Beijing Institute of Technology (CN), Zhuhai Institute of Advanced Technology (CN), University of Science and Technology Beijing (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation
Openalex Percentile: Top 88%
Cooperative Communication and Network Coding
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On Vector Random Linear Network Coding in Wireless Broadcasts — Qifu Tyler Sun, Rina Su, et al. · Entropy (2025) | TGRS Research Map | TGRS