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Practical Network Coding Technologies and Softwarization in Wireless Networks

delete2021-04-01
delete47
PRE
AI
F
Fumin Zhu
C
Chen Zhang *
Z
Zunxin Zheng
A
Ahmed Farouk
DOI:10.1109/JIOT.2021.3056580delete
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摘要

摘要

En 中文
Network coding is an elegant and novel technique to improve network throughput and performance. It is considered as a critical technology to facilitate ever-increasing demands of future wireless networks. It exploits the broadcast nature of wireless media and cooperatively codes packets from different senders to provide reliable, secure, and efficient transmissions. Current research focuses on either transmission delay, coding complexity, forwarding security, or end-to-end throughput. Network coding-aided solutions can recover lost packets without feedback, eliminate latency, reduce the routing cost on diverse paths, or optimize the capacity of unstable wireless networks. However, devices or smart sensors usually have limited computational capacity and some applications could not tolerate high decoding delay, which prevents network coding from being widely deployed in the real world. In recent years, many research methods consider simplifying decoding matrix or coding algorithm to alleviate the shortcoming of network coding and further satisfy the extreme demands of the future wireless network. This article summarizes complexity-optimized methods and explains the interaction effect of coding opportunities and decoding overhead. We propose a taxonomy of practical network coding methods and illustrate three practical directions on cutting computational complexity and enhancing progressive decoding. We also conclude the benefit and cost of current network coding algorithms along with the outline of future research.
Keyword:
Network coding
Encoding
Decoding
Wireless networks
Throughput
Receivers
Packet loss
Instantly decodable network coding
sixth-generation wireless network
sparse network coding
unequal error protection network coding
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期刊

IEEE Internet of Things Journal 封面图
IEEE Internet of Things Journal
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8.9
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1.4W
被引数:
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机构

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Wilfrid Laurier University
学者数:
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论文数: 2.3K
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shenzhen university
学者数:
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被引数: 72
引用论文

引用论文

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