Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85380
Title: Batched network coding with adaptive recoding for multi-hop erasure channels with memory
Authors: Xu, Xiaoli
Guan, Yong Liang
Zeng, Yong
Keywords: Batched Network Coding
Multi-hop Erasure Network
Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Xu, X., Guan, Y. L., & Zeng, Y. (2018). Batched Network Coding With Adaptive Recoding for Multi-Hop Erasure Channels With Memory. IEEE Transactions on Communications, 66(3), 1042-1052. doi:10.1109/TCOMM.2017.2765641
Series/Report no.: IEEE Transactions on Communications
Abstract: In this paper, we study the achievable throughput of batched temporal network coding in multi-hop erasure channels, where network coding is applied only within small coding blocks and each communication hop is modeled as a Gilbert-Elliott (GE) packet erasure channel. The GE channel is a 2-state Markov model that is commonly used for channels with memory. While channel memory does not affect the end-to-end capacity of multi-hop erasure channels, we show that it degrades the end-to-end throughput, when batched network coding with finite batch size is applied, due to the higher variance in erasures within one coding block. On the other hand, if the initial channel state information is available, the channel variance can be significantly reduced. We show that this fact can be utilized for improving the efficiency of the recoding operations at the intermediate nodes, and hence improve the end-to-end throughput of batched network coding schemes. Specifically, we propose adaptive recoding operations, where the network coded packets are adaptively generated based on the number of received packets and the initial channel state for each coding block. The simulation results show that the proposed adaptive recoding scheme significantly enhances the end-to-end throughput of batched network coding over multi-hop GE channels.
URI: https://hdl.handle.net/10356/85380
http://hdl.handle.net/10220/49220
ISSN: 0090-6778
DOI: http://dx.doi.org/10.1109/TCOMM.2017.2765641
Rights: © 2017 IEEE. All rights reserved.
metadata.item.grantfulltext: none
metadata.item.fulltext: No Fulltext
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