A maximum likelihood decoding algorithm of Gabidulin codes in deterministic network coding

Koki Kazama, Akira Kamatsuka, Toshiyasu Matsushima

Abstract: Recently, studies of linear network coding have attracted attention. This paper considers a probabilistic error model for the deterministic linear network coding. In the previous studies of error-correcting codes, Kaneko et al. proposed a decoding algorithms for probabilistic error model. This is a maximum likelihood decoding algorithm that uses the optimization method called branch and bound method. This paper constructs a new model of deterministic linear network coding and proposes a maximum likelihood decoding algorithm that uses the branch and bound method.