BAR: Blockwise Adaptive Recoding for Batched Network Coding

Multi-hop networks become popular network topologies in various emerging Internet of things applications. Batched network coding (BNC) is a solution to reliable communications in such networks with packet loss. By grouping packets into small batches and restricting recoding to the packets belonging...

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Published in:arXiv.org
Main Authors: Yin, Hoover H F, Yang, Shenghao, Zhou, Qiaoqiao, Yung, Lily M L, Ng, Ka Hei
Format: Paper
Language:English
Published: Ithaca Cornell University Library, arXiv.org 17.05.2021
Subjects:
Algorithms
Coding
Feedback
Internet of Things
Network topologies
Packets (communication)
Systems analysis
ISSN:2331-8422
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Abstract Multi-hop networks become popular network topologies in various emerging Internet of things applications. Batched network coding (BNC) is a solution to reliable communications in such networks with packet loss. By grouping packets into small batches and restricting recoding to the packets belonging to the same batch, BNC has a much smaller computational and storage requirements at the intermediate nodes compared with a direct application of random linear network coding. In this paper, we propose a practical recoding scheme called blockwise adaptive recoding (BAR) which learns the latest channel knowledge from short observations so that BAR can adapt to the fluctuation of channel conditions. We focus on investigating practical concerns such as the design of efficient BAR algorithms. We also design and investigate feedback schemes for BAR under imperfect feedback systems. Our numerical evaluations show that BAR has significant throughput gain for small batch size compared with the existing baseline recoding scheme. More importantly, this gain is insensitive to inaccurate channel knowledge. This encouraging result suggests that BAR is suitable to be realized in practice as the exact channel model and its parameters could be unknown and subject to change from time to time.
AbstractList Multi-hop networks become popular network topologies in various emerging Internet of things applications. Batched network coding (BNC) is a solution to reliable communications in such networks with packet loss. By grouping packets into small batches and restricting recoding to the packets belonging to the same batch, BNC has a much smaller computational and storage requirements at the intermediate nodes compared with a direct application of random linear network coding. In this paper, we propose a practical recoding scheme called blockwise adaptive recoding (BAR) which learns the latest channel knowledge from short observations so that BAR can adapt to the fluctuation of channel conditions. We focus on investigating practical concerns such as the design of efficient BAR algorithms. We also design and investigate feedback schemes for BAR under imperfect feedback systems. Our numerical evaluations show that BAR has significant throughput gain for small batch size compared with the existing baseline recoding scheme. More importantly, this gain is insensitive to inaccurate channel knowledge. This encouraging result suggests that BAR is suitable to be realized in practice as the exact channel model and its parameters could be unknown and subject to change from time to time.
Author Ng, Ka Hei
Yang, Shenghao
Yung, Lily M L
Yin, Hoover H F
Zhou, Qiaoqiao
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Snippet Multi-hop networks become popular network topologies in various emerging Internet of things applications. Batched network coding (BNC) is a solution to...
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SubjectTerms Algorithms
Coding
Feedback
Internet of Things
Network topologies
Packets (communication)
Systems analysis
Title BAR: Blockwise Adaptive Recoding for Batched Network Coding
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