TU Berlin

Department of Telecommunication SystemsDr.-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. 


Set-Theoretic Learning for Detection in Cell-Less C-RAN System
Citation key Awanset2018
Author D. A. Awan and R. L.G. Cavalcante and Z. Utkovski and S. Stanczak
Year 2018
Journal IEEE Global Conference on Signal and Information Processing, California, USA, Nov. 26-29, 2018
Abstract Cloud-radio access network (C-RAN) can enable cell-less operation by connecting distributed remote radio heads (RRHs) via fronthaul links to a powerful central unit. In the conventional C-RAN, baseband signals are forwarded after quantization/compression to the central unit for centralized processing/detection in order to keep the complexity of the RRHs low. However, the limited capacity of the fronthaul is a significant bottleneck that prevents C-RAN from supporting large systems (e.g. massive machine-type communications (mMTC)). We propose a learning-based C-RAN in which the detection is performed locally at each RRH and, in contrast to the conventional C-RAN, only the likelihood information is conveyed to the central unit. To this end, we develop a general set-theoretic learning method for estimating likelihood functions. Our method can be used to extend existing detection methods to the C-RAN setting.
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