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. 

Publications

Detection for 5G-NOMA: An Online Adaptive Machine Learning Approach
Zitatschlüssel detection_stanczak2018
Autor D. A. Awan and R.L.G. Cavalcante and M. Yukawa and S. Stanczak
Buchtitel IEEE International Conference on Communications (ICC), Kansas City, MO, USA.
Jahr 2018
Ort Kansas City, MO, USA
Adresse Kansas City, MO, USA
Monat May 20-24
Zusammenfassung Non-orthogonal multiple access (NOMA) has emerged as a promising radio access technique for enabling the performance enhancements promised by the fifth-generation (5G) networks in terms of connectivity, low latency, and high spectrum efficiency. In the NOMA uplink, successive interference cancellation (SIC) based detection with device clustering has been suggested. In the case of multiple receive antennas, SIC can be combined with the minimum mean-squared error (MMSE) beamforming. However, there exists a tradeoff between the NOMA cluster size and the incurred SIC error. Larger clusters lead to larger errors but they are desirable from the spectrum efficiency and connectivity point of view. We propose a novel online learning based detection for the NOMA uplink. In particular, we design an online adaptive filter in the sum space of linear and Gaussian reproducing kernel Hilbert spaces (RKHSs). Such a sum space design is robust against variations of a dynamic wireless network that can deteriorate the performance of a purely nonlinear adaptive filter. We demonstrate by simulations that the proposed method outperforms the MMSE-SIC based detection for large cluster sizes.
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