Update-Based Machine Learning Classification of Hierarchical Symbols in a Slowly Varying Two-Way Relay Channel

Mathematics

Jakub Kolář¹  Jan Sýkora¹  Petr Hron¹ 

[1] Department of radio engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 160 00 Prague, Czech Republic;

关键词: wireless physical-layer network coding;    machine learning;    artificial neural networks;    backpropagation;    two-way relay channel;    decision regions;   

DOI  :  10.3390/math8112007

来源: DOAJ

【 摘 要 】

This paper presents a stochastic inference problem suited to a classification approach in a time-varying observation model with continuous-valued unknown parameterization. The utilization of an artificial neural network (ANN)-based classifier is considered, and the concept of a training process via the backpropagation algorithm is used. The main objective is the minimization of resources required for the training of the classifier in the parametric observation model. To reach this, it is proposed that the weights of the ANN classifier vary continuously with the change of the observation model parameters. This behavior is then used in an update-based backpropagation algorithm. This proposed idea is demonstrated on several procedures, which re-use previously trained weights as prior information when updating the classifier after a channel phase change. This approach successfully saves resources needed for re-training the ANN. The new approach is verified via a simulation on an example communication system with the two-way relay slowly fading channel.

【 授权许可】

Unknown