Convolutional Network Coding Based on Matrix Power Series Representation
Network coding was formally introduced by. Later, linear network coding was proved to be able to achieve the optimal data transmission rate in an acyclic multicast network, and an algebraic approach to linear network coding was presented in. Since then, a rich literature on linear network coding has emerged, and a wide variety of applications have been developed. In this paper, convolutional network coding is formulated by means of matrix power series representation of the Local Encoding Kernel (LEK) matrices and Global Encoding Kernel (GEK) matrices to establish its theoretical fundamentals for practical implementations.