Biography

Dr. Fatma is a research fellow in the AESE research group at Imperial College London since 2021. She was a research associate in the AESE research group at Imperial College London since 2018. She obtained her PhD in Electrical Engineering in 2017 from King Abdullah University of Science and Technology (KAUST), Saudi Arabia. She also obtained her Master of Science from KAUST in January 2013. She graduated as a Polytechnician engineer from “Ecole Polytechnique de Tunis” with major in Signals and Systems.

Her research interest is in the general area of wireless communication systems, and in the particular research areas of wireless sensor networks (WSN), low power area networks (LPWAN), and 5G networks. Her research program aims to model, schedule, optimize, manage and assess the performance of these subjects. Her research approach emphasizes exploiting statistical propagation models in a mathematically rigorous manner, and the numerical simulation of our theories. To date, her research can be categorized under 4 themes:

• Performance limits of fading channels in the low power regime:
  • Rician fading, Nakagami fading, Gamma-Gamma fading, MIMO system, Log-Normal fading, imperfect channel state information (CSI)
• Radio frequency energy harvesting (EH) and simultaneous wireless information and power transfer:
  • Precoding design of MIMO relay systems with EH relay and possibly imperfect CSI
  • Performance analysis of radio cognitive networks using antenna switching technique
  • Spatiotemporal modeling of self-powered Device-to-Device networks using two-dimensional Discrete-time Markov chain (DTMC)
• Low power wide area networks (LPWANs) and LoRa networks
  • Fundamental understanding of LoRa physical layer
  • Harvest-then-transmit protocol for EH-LPWANs
  • Nonorthogonal multiple access (NOMA) scheme for LPWANs
  • Spatiotemporal modeling for LPWANs with duty cycling
• Key generation scheme for Physical layer security
• Freshness of information in wireless sensor networks (WSNs).
  • Scheduling policies for multihop WSN networks with EH capabilities
  • Reinforcement learning approach for relay system with EH source