Optimization of data transmission to the mobile device: Network layer

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.

 

 

Examples of topics dealt with in the Wireless Communications and Networks department include: learning of so-called radio maps using measured user data [1], [2], [3], [4] and exploiting spatial and spectral correlation to estimate the spatial distribution of path loss [5].

References

[1] M. Kasparick, R. L. G. Cavalcante, S. Valentin, S. Stańczak, and M. Yukawa, "Kernel-Based Adaptive Online Reconstruction of Coverage Maps with Side Information," IEEE Transactions on Vehicular Technology, vol. 65, no. 7, pp. 5461-5473, July 2016

[2] 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

[3] 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)

[4] 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

[5] 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