TU Berlin

Institut für TelekommunikationssystemeDr.-Ing. Daniyal Amir Awan


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Dr.-Ing. Daniyal Amir Awan

I have doctorate 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. 


Robust Cell-Load Learning with a Small Sample Set
Zitatschlüssel Robawan2019
Autor D. A. Awan and R. L.G. Cavalcante and S. Stanczak
Seiten 68:270-283
Jahr 2020
DOI 10.1109/TSP.2019.2959221
Journal IEEE Transactions on Signal Processing (TSP)
Jahrgang 68
Nummer 68
Monat Jan.
Zusammenfassung Learning of the cell-load in radio access networks (RANs) has to be performed within a short time period. Therefore, we propose a learning framework that is robust against uncertainties resulting from the need for learning based on a relatively small training set. To this end, we incorporate prior knowledge about the cell-load in the learning framework. For example, an inherent property of the cell-load is that it is monotonic in downlink (data) rates. To obtain additional prior knowledge we first study the feasible rate region, i.e., the set of all vectors of user rates that can be supported by the network. We prove that the feasible rate region is compact. Moreover, we show the existence of a Lipschitz function that maps feasible rate vectors to cell-load vectors. With these results in hand, we present a learning technique that guarantees a minimum approximation error in the worst-case scenario by using prior knowledge and a small training sample set. Simulations in the network simulator NS3 demonstrate that the proposed method exhibits better robustness and accuracy than standard learning techniques, especially for small training sample sets.
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