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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. 

Publications

Machine Learning-Based Adaptive Receive Filtering: Proof-of-Concept on an SDR Platform
Citation key Mehl2019ICC
Author Matthias Mehlhose, Daniyal Amir Awan, Renato L.G. Cavalcante, Martin Kurras and Slawomir Stanczak
Year 2019
Journal arXiv:1911.04291, submitted to ICC 2020, 11 Nov. 2019
Abstract Conventional multiuser detection techniques either require a large number of antennas at the receiver for a desired performance, or they are too complex for practical implementation. Moreover, many of these techniques, such as successive interference cancellation (SIC), suffer from errors in parameter estimation (user channels, covariance matrix, noise variance, etc.) that is performed before detection of user data symbols. As an alternative to conventional methods, this paper proposes and demonstrates a low-complexity practical Machine Learning (ML) based receiver that achieves similar (and at times better) performance to the SIC receiver. The proposed receiver does not require parameter estimation; instead it uses supervised learning to detect the user modulation symbols directly. We perform comparisons with minimum mean square error (MMSE) and SIC receivers in terms of symbol error rate (SER) and complexity.
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Daniyal Amir Awan
Wissenschaftlicher Mitarbeiter
Network Information Theory