IEICE TRANSACTIONS on Communications
MANET Multicast Model with Poisson Distribution and Its Performance for Network Coding
Summary :
Network Coding (NC) can improve the information transmission efficiency and throughput of data networks. Random Linear Network Coding (RLNC) is a special form of NC scheme that is easy to be implemented. However, quantifying the performance gain of RLNC over conventional Store and Forward (S/F)-based routing system, especially for wireless network, remains an important open issue. To solve this problem, in this paper, based on abstract layer network architecture, we build a dynamic random network model with Poisson distribution describing the nodes joining the network randomly for tree-based single-source multicast in MANET. We then examine its performance by applying conventional Store and Forward with FEC (S/F-FEC) and RLNC methods respectively, and derive the analytical function expressions of average packet loss rate, successful decoding ratio and throughput with respect to the link failure probability. An experiment shows that these expressions have relatively high precision in describing the performance of RLNC. It can be used to design the practical network coding algorithm for multi-hop multicast with tree-based topology in MANET and provide a research tool for the performance analysis of RLNC.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E94-B No.3 pp.823-826
- Publication Date
- 2011/03/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E94.B.823
- Type of Manuscript
- LETTER
- Category
- Network
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Song XIAO, Ji LU, Ning CAI, "MANET Multicast Model with Poisson Distribution and Its Performance for Network Coding" in IEICE TRANSACTIONS on Communications,
vol. E94-B, no. 3, pp. 823-826, March 2011, doi: 10.1587/transcom.E94.B.823.
Abstract: Network Coding (NC) can improve the information transmission efficiency and throughput of data networks. Random Linear Network Coding (RLNC) is a special form of NC scheme that is easy to be implemented. However, quantifying the performance gain of RLNC over conventional Store and Forward (S/F)-based routing system, especially for wireless network, remains an important open issue. To solve this problem, in this paper, based on abstract layer network architecture, we build a dynamic random network model with Poisson distribution describing the nodes joining the network randomly for tree-based single-source multicast in MANET. We then examine its performance by applying conventional Store and Forward with FEC (S/F-FEC) and RLNC methods respectively, and derive the analytical function expressions of average packet loss rate, successful decoding ratio and throughput with respect to the link failure probability. An experiment shows that these expressions have relatively high precision in describing the performance of RLNC. It can be used to design the practical network coding algorithm for multi-hop multicast with tree-based topology in MANET and provide a research tool for the performance analysis of RLNC.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E94.B.823/_p
Copy
@ARTICLE{e94-b_3_823,
author={Song XIAO, Ji LU, Ning CAI, },
journal={IEICE TRANSACTIONS on Communications},
title={MANET Multicast Model with Poisson Distribution and Its Performance for Network Coding},
year={2011},
volume={E94-B},
number={3},
pages={823-826},
abstract={Network Coding (NC) can improve the information transmission efficiency and throughput of data networks. Random Linear Network Coding (RLNC) is a special form of NC scheme that is easy to be implemented. However, quantifying the performance gain of RLNC over conventional Store and Forward (S/F)-based routing system, especially for wireless network, remains an important open issue. To solve this problem, in this paper, based on abstract layer network architecture, we build a dynamic random network model with Poisson distribution describing the nodes joining the network randomly for tree-based single-source multicast in MANET. We then examine its performance by applying conventional Store and Forward with FEC (S/F-FEC) and RLNC methods respectively, and derive the analytical function expressions of average packet loss rate, successful decoding ratio and throughput with respect to the link failure probability. An experiment shows that these expressions have relatively high precision in describing the performance of RLNC. It can be used to design the practical network coding algorithm for multi-hop multicast with tree-based topology in MANET and provide a research tool for the performance analysis of RLNC.},
keywords={},
doi={10.1587/transcom.E94.B.823},
ISSN={1745-1345},
month={March},}
Copy
TY - JOUR
TI - MANET Multicast Model with Poisson Distribution and Its Performance for Network Coding
T2 - IEICE TRANSACTIONS on Communications
SP - 823
EP - 826
AU - Song XIAO
AU - Ji LU
AU - Ning CAI
PY - 2011
DO - 10.1587/transcom.E94.B.823
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E94-B
IS - 3
JA - IEICE TRANSACTIONS on Communications
Y1 - March 2011
AB - Network Coding (NC) can improve the information transmission efficiency and throughput of data networks. Random Linear Network Coding (RLNC) is a special form of NC scheme that is easy to be implemented. However, quantifying the performance gain of RLNC over conventional Store and Forward (S/F)-based routing system, especially for wireless network, remains an important open issue. To solve this problem, in this paper, based on abstract layer network architecture, we build a dynamic random network model with Poisson distribution describing the nodes joining the network randomly for tree-based single-source multicast in MANET. We then examine its performance by applying conventional Store and Forward with FEC (S/F-FEC) and RLNC methods respectively, and derive the analytical function expressions of average packet loss rate, successful decoding ratio and throughput with respect to the link failure probability. An experiment shows that these expressions have relatively high precision in describing the performance of RLNC. It can be used to design the practical network coding algorithm for multi-hop multicast with tree-based topology in MANET and provide a research tool for the performance analysis of RLNC.
ER -
English
