
M. Reza Khanzadi
Ph.D. Candidate
I am currently a Ph.D. candidate at the Department of Signals and Systems in collaboration with the Department of Micro-Technology and Nano-Science, Chalmers University of Technology.
- M. Reza khanzadi
- June 09, 1983
- khanzadi@ieee.org
- www.khanzadi.info
- Soft metrics and their Performance Analysis for Optimal Data Detection in the Presence of Strong Oscillator Phase Noise
- Improving Bandwidth Efficiency in E-band
Communication Systems
- Effect of Synchronizing Coordinated Base Stations on Phase Noise Estimation
- A model-based analysis of phase jitter in RF oscillators
- Variational Bayesian Framework for Receiver Design in the Presence of Phase Noise in MIMO Systems
Khanzadi, Mohammad Reza: Uncoded cognitive transmission over AWGN and fading channels. Göteborg : Chalmers University of Technology. (Ex - Institutionen för signaler och system, Chalmers tekniska högskola;EX068(2010)
Tahmasebi Toyserkani, Arash; Khanzadi, Mohammad Reza; Ström, Erik G.; Svensson, Arne: A Complexity Adjustable Scheduling Algorithm for Throughput Maximization in Clusterized TDMA Networks. Proceedings IEEE Vehicular Technology Conference Spring, Taipei, Taiwan
I am currently a Ph.D. candidate at the Department of Signals and Systems in collaboration with the Department of Micro-Technology and Nano-Science, Chalmers University of Technology.
Bayesian inference, statistical signal processing, and communication theory are my main research interests. My current project is focused on RF oscillator modeling, phase noise estimation/compensation, and determining the effect of phase noise on the performance of communication systems. I have also conducted research in the area of cognitive radio networks during my master studies.
In the era of big data, big thinking is needed.
My new research hobbies are machine learning and data analysis. As a researcher with a curious mind and the passion for working with new challenging problems, the design of intelligent machine learning algorithms is an exciting area for me to explore. I believe since there is not any limit to the amount of data that we will produce, there will be no end to the need for faster, more accurate and less noise sensitive algorithms of learning and prediction.
Contact info
- Communication Systems Group
- Department of Signals and Systems
- Chalmers University of Technology
- SE-412 96 Gothenburg, SWEDEN.
- Email: khanzadi@ieee.org
- Website: www.khanzadi.info