Jonathan Ah Sue



Organisationseinheit


Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design)


Publikationen (Download BibTeX)


Ah Sue, J., Brand, P., Falk, J., Hasholzner, R., & Teich, J. (2019). Optimizing Exploratory Workflows for Embedded Platform Trace Analysis and its Application to Cellular Modems (to appear). In HCII 2019 Late Breaking Work Papers Proceedings (pp. 1-12). Orlando, Florida, USA, US: New York, NY, USA: Springer.
Nisar, A., Ah Sue, J., & Teich, J. (2019). Performance Comparison between Machine Learning based LTE Downlink Grant Predictors. In Proceedings of the 21st International Conference on Artificial Intelligence. Las Vegas, US.
Ah Sue, J., Brand, P., Brendel, J., Hasholzner, R., Falk, J., & Teich, J. (2018). A Predictive Dynamic Power Management for LTE-Advanced Mobile Devices. In IEEE (Eds.), 2018 IEEE Wireless Communications and Networking Conference (WCNC'18). Barcelona, Catalonia, Spain, ES.
Ah Sue, J., Hasholzner, R., & Brendel, J. (2018). Improvements in LTE-Advanced Time Series Prediction with Dimensionality Reduction Algorithms. In IEEE (Eds.), Proc. of the IEEE 5G World Forum (pp. 1-10). Santa Clara, CA, US.
Brand, P., Falk, J., Ah Sue, J., Brendel, J., Hasholzner, R., & Teich, J. (2018). Reinforcement Learning for Power-Efficient Grant Prediction in LTE. In ACM (Eds.), 21st International Workshop on Software and Compilers for Embedded Systems (SCOPES’18) (pp. 18-26). Sankt Goar, DE.
Brand, P., Ah Sue, J., Brendel, J., Falk, J., Hasholzner, R., Teich, J., & Wildermann, S. (2017). Exploiting Predictability in Dynamic Network Communication for Power-Efficient Data Transmission in LTE Radio Systems. In ACM (Eds.), 20th International Workshop on Software and Compilers for Embedded Systems (SCOPES’17) (pp. 64 - 67). Sankt Goar, Deutschland, DE.
Ah Sue, J., Hasholzner, R., Brendel, J., Kleinsteuber, M., & Teich, J. (2016). A Binary Time Series Model of LTE Scheduling for Machine Learning Prediction. In 1st International Workshops on Foundations and Applications of Self-Adaptive and Self-Organizing Systems (SASO 2016)Self-Organizing Systems (SASO 2016). Augsburg, DE.

Zuletzt aktualisiert 2017-29-01 um 04:04