Much of the relevant data is only available on the network layer and thus allows complete statements to be made about the data rate and data quality achieved.
Since these are generally mobile devices, local distributions of various relevant parameters can be obtained from the data of the network layer, coupled with environmental data and the exploitation of other sources of information. This is shown schematically in the figure below, where Machine Learning methods can be used effectively, particularly because longer-term learning is possible. Hence, location-dependent additional information is obtained for the respective communication cell and parts of its adjacent neighboring cells. This allows a predictable behavior for the mobile subscribers and the wireless network to be better exploited.
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 K. Oltmann, R. L. G. Cavalcante, S. Stańczak, and M. Kasparick, "Interference Identification in Cellular Networks via Adaptive Projected Subgradient Methods," in Proc. IEEE Asilomar Conference on Signals, Systems, and Computers, Nov. 2013
 Z. Utkovski, P. Agostini, M. Frey, I. Bjelakovic, and S. Stanczak. Learning radio maps for physical-layer security in the radio access. IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), July 2019 (invited)
 M.A. Gutierrez-Estevez, R.L.G. Cavalcante, and S. Stanczak. Nonparametric radio maps reconstruction via elastic net regularization with multi-kernels. IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2018
 D. Schäufele, et.al. “Tensor Completion for Radio Map Reconstruction and Channel Cartography using Low Rank and Smoothness“, IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), July 2019