Page Content
to Navigation
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.
Publications
| 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. |