To address the low accuracy of linear correlation probability performance indicators in current sparse network coding research,we propose a performance analysis model based on Markov chain.The performance indicators such as line-ar correlation probability and rank probability distribution and their complexity are analyzed,and the decoding success probability in the later stage of encoding packet transmission is analyzed through this performance analysis model.The mod-el is based on absorbing Markov chain to compute the transient state,absorbing state and state transition probabilities dur-ing encoding packet transmission.Monte Carlo simulation errors in state transition probabilities are improved.Further,the basic matrix of absorbing Markov chain is constructed from state transition probabilities,and the linear correlation probabili-ty of non-innovative packets at receiver is obtained.The rank probability distribution and the decoding probability are de-duced.Simulation results indicate that the performance metrics of this model are more accurate than those of other research models under the same conditions,and the decoding behavior of sparse network coding,such as the probability of rank dis-tribution of the decoded matrix and the probability of decoding success,can be evaluated accurately.
关键词
网络编码/稀疏网络编码/吸收马尔可夫链模型/线性相关概率/秩的概率分布
Key words
network coding/sparse network coding/absorbing Markov chain mode/linear correlation probability/probabil-ity of rank distribution
维普
万方数据