01964nab a2200265 i 4500 acta50097 20220620154138.0 180213s2017 hu o 0|| eng d 0324-721X 10.14232/actacyb.23.2.2017.17 doi SZTE Egyetemi Kiadványok Repozitórium hun eng Tömösközi Máté Prediction of RoHCv1 and RoHCv2 compressor utilities for VoIP [elektronikus dokumentum] / Tömösközi Máté 2017 737-756 Acta cybernetica 23 No. 2 Modern cellular networks utilising the long–term evolution (LTE) and the coming 5G set of standards face an ever–increasing demand for low–latency mobile data from connected devices. Header compression is employed to minimise the overhead for IP–based cellular network traffic, thereby decreasing the overall bandwidth usage and, subsequently, transmission delays. Here, we employ machine learning approaches for the prediction of Robust Header Compression version 1’s and version 2’s compression utility for VoIP transmissions, which allows the compression to dynamically adapt to varying channel conditions. We evaluate various regression models employing r2 and mean square error scores next to complexity (number of coefficients) based on an RTP specific training data set and separately captured live VoIP audio calls. We find that the proposed weighted Ridge regression model explains about at least 50 % of the observed results and the accuracy score may be as high as 94 % for some of the VoIP transmissions. Természettudományok Számítás- és információtudomány Informatika, Számítástechnika, Regresszió - lineáris Seeling Patrick aut Ekler Péter aut Fitzek Frank H. P. aut http://acta.bibl.u-szeged.hu/50097/1/actacyb_23_2_2017_17.pdf Dokumentum-elérés