Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. A Robust Machine Learning Method for CellLoad Approximation in Wireless Networks
 Institute of Electrical and Electronics Engineers IEEE; IEEE Signal Processing Society: IEEE International Conference on Acoustics, Speech, and Signal Processing 2018. Proceedings : April 1520, 2018, Calgary Telus Convention Center, Calgary, Alberty, Canada Piscataway, NJ: IEEE, 2018 ISBN: 9781538646588 ISBN: 9781538646571 ISBN: 9781538646595 S.26012605 
 International Conference on Acoustics, Speech, and Signal Processing (ICASSP) <2018, Calgary> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer HHI () 
Abstract
We propose a learning algorithm for cellload approximation in wireless networks. The proposed algorithm is robust in the sense that it is designed to cope with the uncertainty arising from a small number of training samples. This scenario is highly relevant in wireless networks where training has to be performed on short time scales because of a fast timevarying communication environment. The first part of this work studies the set of feasible rates and shows that this set is compact. We then prove that the mapping relating a feasible rate vector to the unique fixed point of the nonlinear cellload mapping is monotone and uniformly continuous. Utilizing these properties, we apply an approximation framework that achieves the best worstcase performance. Furthermore, the approximation preserves the monotonicity and continuity properties. Simulations show that the proposed method exhibits better robustness and accuracy for small training sets in comparison with standard approximation techniques for multivariate data.