%0 Conference Paper %1 seb20ITC32 %A Lindner, Sebastian %A Fisser, Leonard %A Timm-Giel, Andreas %B 32th International Teletraffic Congress (ITC 32) %C Osaka, Japan %D 2020 %K dynamic_spectrum_access itc itc32 machine_learning neural_networks reliability %T Coexistence of Shared-Spectrum Radio Systems through Medium Access Pattern Learning using Artificial Neural Networks %U https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc32/seb20ITC32.pdf?inline=true %X Abstract—Spectrum scarcity requires novel approaches for sharing frequency resources between different radio systems. Where coordination is not possible, intelligent approaches are needed, allowing a novel ”secondary” system to access unused resources of a legacy (primary) system without requiring modifications of this primary system. Machine Learning is a promising approach to recognize patterns of the primarysystem and adapt the channel access accordingly. In this contribution we investigate the capability of Feed-Forward Deep Learning and Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) to detect communication patterns of the primary user. Therefore we take the example of a new aeronautical system (LDACS) coexisting with three different systems. Firstly the coexistence with the Distance Measurement Equipment (DME) providing a deterministic interference to the secondary user and secondly with two synthetic channel access patterns, realized by a 2-state Markov model, modeling a bursty channel access behavior, as well as through a sequential channel access model. It can be shown that the Markov property of a Gilbert-Elliot channel model limits the predictability; nonetheless, we show that the model characteristics can be fully learned, which could leverage the design of interference avoidance systems that make use of this knowledge. The determinism of DME allows an error-free prediction, and it is shown that the reliability of sequential access model prediction depends on the model’s parameter. The limits of Feed-Forward Deep Neural Networks are highlighted, and why LSTM RNNs are state-of-the-art models in this problem domain. We show that these models are capable of online learning, as well as of learning correlations over long periods of time. In the spirit of open science, the implementation files are made available in the conclusion.

@inproceedings{seb20ITC32, abstract = {Abstract—Spectrum scarcity requires novel approaches for sharing frequency resources between different radio systems. Where coordination is not possible, intelligent approaches are needed, allowing a novel ”secondary” system to access unused resources of a legacy (primary) system without requiring modifications of this primary system. Machine Learning is a promising approach to recognize patterns of the primarysystem and adapt the channel access accordingly. In this contribution we investigate the capability of Feed-Forward Deep Learning and Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) to detect communication patterns of the primary user. Therefore we take the example of a new aeronautical system (LDACS) coexisting with three different systems. Firstly the coexistence with the Distance Measurement Equipment (DME) providing a deterministic interference to the secondary user and secondly with two synthetic channel access patterns, realized by a 2-state Markov model, modeling a bursty channel access behavior, as well as through a sequential channel access model. It can be shown that the Markov property of a Gilbert-Elliot channel model limits the predictability; nonetheless, we show that the model characteristics can be fully learned, which could leverage the design of interference avoidance systems that make use of this knowledge. The determinism of DME allows an error-free prediction, and it is shown that the reliability of sequential access model prediction depends on the model’s parameter. The limits of Feed-Forward Deep Neural Networks are highlighted, and why LSTM RNNs are state-of-the-art models in this problem domain. We show that these models are capable of online learning, as well as of learning correlations over long periods of time. In the spirit of open science, the implementation files are made available in the conclusion.}, added-at = {2021-03-02T15:22:25.000+0100}, address = {Osaka, Japan}, author = {Lindner, Sebastian and Fisser, Leonard and Timm-Giel, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/2d782c739efa2e85092206f81e3fddb3a/itc}, booktitle = {32th International Teletraffic Congress (ITC 32)}, interhash = {31e7f757fbca4375a68dcd2dfbf25d11}, intrahash = {d782c739efa2e85092206f81e3fddb3a}, keywords = {dynamic_spectrum_access itc itc32 machine_learning neural_networks reliability}, timestamp = {2021-03-02T15:22:25.000+0100}, title = {Coexistence of Shared-Spectrum Radio Systems through Medium Access Pattern Learning using Artificial Neural Networks}, url = {https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc32/seb20ITC32.pdf?inline=true}, year = 2020 }