MANET Multicast Model with Poisson Distribution and Its Performance for Network Coding
スポンサーリンク
概要
- 論文の詳細を見る
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.
論文 | ランダム
- コートディヴォワール/「和解フォーラム」後の課題 (特集 国民和解--圧政・内戦・虐殺を超えて)
- トレンド・リポート コートディヴォワール/「病める地域大国」の政治課題
- 分析リポート コートディヴォワールの政治危機--争点なき多党制の閉塞
- 書評 日本教師教育学会編『日本の教師教育改革』
- 樋口 勘次郎,小西 重直から学ぶ労作教育論