A Decentralized Approach to Network Coding Based on Learning

Abstract

Network coding is used to efficiently transmit information in a network from source nodes to sink nodes through intermediate nodes. It has been shown that linear coding is sufficient to achieve the multicast network capacity. In this paper, we introduce a method to design capacity achieving network codes based on reinforcement learning and makes using the market theory concepts. We demonstrate that the proposed algorithm is decentralized and polynomial time complex; while it constructs the codes much faster than other random methods with the same complexity order, especially in large networks with small field sizes. Furthermore, the proposed algorithm is robust to link failures and is used to reduce the number of encoding nodes in the network.