Wireless communication is a transformative technology that has changed the way we work, communicate and enjoy our free time. The number of connected devices is expected to increase to 29.4 billion by 2030. As a result, there is a demand for higher data rates, lower delays and constant connectivity of wireless devices. These demands have driven the networking community to seek new technologies as these requirements are beyond the capabilities of current networks. Communication at higher frequencies, beyond 10 GHz where most of current communications systems operate, could be the game changing technology for the next generation of networks. At millimeter-wave frequencies, 30-300 GHz, the available spectrum is larger than all spectrum currently allocated to cellular and wireless area networks (WLAN). The unlicensed spectrum at 60 GHz alone can offer 10 to 100 times more spectrum than it is available in current unlicensed WLANs. The larger bandwidth allocations allow for increased datarates. These multi-gigabit per second rates and milisecond latencies are now achievable thanks to new directional high gain antennas and cost-effective CMOS technology that can operate at mm-wave frequencies.
However, with the use of this new technology new challenges arise.
The thesis is divided into two parts, first we study the challenges in the optimization and features of conventional mm-wave networks, and second, we consider a new way of designing mm-wave communications using Machine Learning.
Dolores Garcia Marti is a PhD student in Telematics at the University of Carlos III. Her research interest is in the intersection between machine learning and mm-wave communications. Previously, she completed an MSc in Theoretical Physics at Imperial College London and a BS in Mathematics at the University of Valencia.
PhD Thesis Advisor: Dr. Joerg Widmer, IMDEA Networks Institute, Spain
University: University Carlos III of Madrid, Spain
Doctoral Program: Telematics Engineering
PhD Committee members: