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Machine learning for wireless communication systems

Project

Project Details

Program
Electrical Engineering
Field of Study
Electrical and Computer Engineering
Division
Computer, Electrical and Mathematical Sciences and Engineering

Project Description

At the CCSL, we are engaged in research and teaching on wireless communication methods for future wireless communication systems. In future wireless communication 5G and beyond, an extremely high number of heterogeneous devices, such as smartphones, sensors, robots, and vehicles, will communicate with each other. Consequently, the need for higher data rates and lower latency will increase significantly, posing major challenges for the resource allocation. Deep learning and data-driven algorithm approximation schemes have recently received significant attention as means to perform resource allocation with reduced complexity in 5G and beyond networks. This project considers the challenging case of Reflective Intelligent System (RIS) assisted, mmWave, frequency selective, massive MIMO systems with hybrid architecture and develops deep-learning based resource allocation frameworks. In these frameworks, prior data-set observations and deep neural network models will be leveraged to learn the mapping from received measurements to channels, beamformers and power allocations. Furthermore, deep neural networks will be used to approximate the optimization problems by selecting the suitable parameters that minimize the approximation error. The usage of a deep neural network framework reduces the computational complexity and processing overhead, since it only requires a limited number of layers of matrix-vector multiplications which can reduce processing time substantially.

About the Researcher

Ahmed Eltawil
Professor, Electrical Engineering

Affiliations

Education Profile

  • Ph.D., University of California, Los Angeles, Integrated Circuits and Systems, 2003
  • M.S., Cairo University, Electronics and Communications Engineering, 1999
  • B.Sc., Cairo University, Electronics and Communications Engineering, 1997

Research Interests

a€‹The current research interests of Dr. Ahmed are in the general area of low power digital circuit and signal processing architectures with an emphasis on mobile systems. He is affiliated with both the Center for Pervasive Communications and Computing (CPCC) and the Center for Embedded and Cyber-Physical Systems (CECS) at the University of California, Irvine.

Selected Publications

  • Publications list on ORCID
  • Publications on Google Scholar
  • Publications on Google Scholar
  • Also view list of Publications on researchgate
  • Publications on IEEEXplore Digital Library

Desired Project Deliverables

Goal: This project aims to improve the overall performance on emerging and beyond 5G wireless systems in boosting system capacity with improved robustness and high data rates, via a low-cost, low-latency, and green implementation. For this purpose, deep learning or machine learning methods shall be applied, which can adapt to the dynamic changes of the wireless communication system and its environment and exploit the past experience to improve the future performance of the system. A report detailing system design and simulation results are expected at the end of the program.

Recommended Student Background

Machine Learning
Wireless communication
optimization and linear algebra