Research on rank distribution analysis model in sparse network coding
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.
network codingsparse network codingabsorbing Markov chain modelinear correlation probabilityprobabil-ity of rank distribution
万方数据
维普
