Intelligent network coding method and equipment based on deep reinforcement learning

Abstract

The invention provides a network coding method based on deep reinforcement learning, and the method comprises the steps that a source node divides to-be-transmitted information into K pieces, determines the coding coefficient of each piece according to a source node coding model, and generates and transmits a coding packet to a next hop node; and an intermediate node receives the coding packet sent by the previous node, codes the received coding packet again, determines a coding coefficient according to an intermediate node coding model, and generates and sends the coding packet to the next hop node, wherein the source node and the intermediate node coding model are obtained by training a DQN network. The method can adaptively adjust the coding coefficient according to the dynamic change of the network, improves the decoding efficiency, has good model generalization capability, can be generalized to networks with different network scales and different link qualities, and executes respective coding coefficient optimization models on the source node and the intermediate node in a distributed manner, so that coding coefficient optimization implementation is simplified, and stability of DQN training is improved.

Description

technical field [0001] The invention relates to the field of information technology, in particular to a network coding method. Background technique [0002] Linear network coding is a class of network coding in which data are linearly combined by coding coefficients selected from finite fields. Compared with nonlinear network coding using nonlinear combination functions, linear network coding has lower complexity and simpler model, so it has been intensively studied and widely used. [0003] The basic idea of ​​linear network coding is that the nodes in the network linearly encode the original data by selecting encoding coefficients from the finite field to form new encoded data and forward it, and the receiving node can recover the original data through the corresponding decoding operation. Linear network coding methods mainly include deterministic network coding algorithms and stochastic linear network coding algorithms. Deterministic network coding algorithms can guaran...

AI Technical Summary

Problems solved by technology

Therefore, this encoding method cannot adjust the encoding coefficients according to the dynamic changes of the network (including changes in the quality of network links and the number of intermediate nodes), resulting in low decoding efficiency.

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