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TU Berlin

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Daniyal Amir Awan

I have a master's degree in electrical engineering from Technical University of Berlin. I work as a research associate at TU Berlin and as a guest researcher at the Wireless Communication and Networks Department, Heinrich Hertz Institute, Berlin. My research revolves around application of optimization theory, function approximation, and machine-learning to problems in wireless communication systems. I am currently working in the following directions:

1. Set-membership & robust function approximation in dynamic wireless networks with a small sample set. 

2. Nonlinear detection for multi-user uplink using the set-membership paradigm.

3. Energy optimization in future wireless networks. 


A Robust Machine Learning Method for Cell-Load Approximation in Wireless Networks
Zitatschlüssel robustmachine_awan2018
Autor D. A. Awan and R.L.G. Cavalcante and S. Stanczak
Buchtitel IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Calgary, Alberta, Canada.
Jahr 2018
Adresse Calgary, Alberta, Canada
Monat April 15-20
Zusammenfassung We propose a learning algorithm for cell-load approximation in wireless networks. The proposed algorithm is robust in the sense that it is designed to cope with the uncertainty arising from a small number of training samples. This scenario is highly relevant in wireless networks where training has to be performed on short time scales because of a fast time-varying communication environment. The first part of this work studies the set of feasible rates and shows that this set is compact. We then prove that the mapping relating a feasible rate vector to the unique fixed point of the non-linear cell-load mapping is monotone and uniformly continuous. Utilizing these properties, we apply an approximation framework that achieves the best worst-case performance. Furthermore, the approximation preserves the monotonicity and continuity properties. Simulations show that the proposed method exhibits better robustness and accuracy for small training sets in comparison with standard approximation techniques for multivariate data.
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Daniyal Amir Awan
Wissenschaftlicher Mitarbeiter
Network Information Theory NetIT
HFT 6-1