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School of Electronic Engineering and Computer Science

Dr Fatma Benkhelifa


Lecturer in Telecommunications



Dr Fatma Benkhelifa is a Lecturer in Telecommunications at Queen Mary University of London. She is a honorary research fellow at Imperial College London. Her research interests include, but not limited to, resource management algorithms, stochastic-geometry-based analysis, spatio-temporal modeling, energy harvesting, physical-layer security, reinforcement learning and deep learning approaches of low-power wide-area networks, wireless sensor networks, and wireless communication systems. 

Before joining QMUL, she was a lecturer at Coventry University and research fellow/research associate at Imperial College London. She received PhD in Electrical Engineering in 2017 (King Abdullah University of Science and Technology (KAUST), Saudi Arabia), Master of Science in Electrical Engineering in 2013 (KAUST, Saudi Arabia), and Polytechnician Engineering Diploma in Signals and Systems in 2011 (Ecole Polytechnique de Tunisie, Tunisia).  

She is an associate editor at IEEE Communications Letters and at Frontiers in Communications and Networks, a reviewer in many IEEE transaction journals and a technical committee member (TPC) in prestigious IEEE conferences such as IEEE ICC, IEEE GLOBECOM, IEEE WCNC, etc.


  • EBU4203 Introduction to AI (BUPT joint programme) 

This course explores the introductory concepts and methods behind artificial intelligence (AI). The course covers the fundamental but most important developments of AI supporting decision making while touching upon the ethics and safety of AI. The key focus is placed on: Expert Systems, Problem Solving, Machine Learning, Knowledge Representation, Reasoning and Decision Making, AI Applications, Ethics and Safety of AI. 

  • EBU6366 Microwave, Millimeter and Optical Transmission (BUPT joint programme) 

The course explores the principles and methods of microwave, millimeter wave and optical transmission. The course covers the design and operation of Smith chart, impedance matching, microwave resonator and other microwave and millimeter wave networks and devices. The course aims to design communication systems based on microwave theory and learn how to optimise communication systems. 

  • EBU6408 Sensors and Radio Frequency Identification (BUPT joint programme) 

This course explores the basic principle of different RFID system components and the related wireless communication principles. The course covers the standard protocols used in RFID tags and UHF RFID protocol standard, RFID-based system design and testing methodologies and metrics and up-to-date application pattern of RFID in Internet of Things applications. 


Research Interests:

Fatma's research has an inter-disciplinary nature exploiting mathematical models to model real-world problems in wireless communication, Internet of Things (IoT), wireless sensor networks (WSNs), 5G and beyond networks, etc. Her research program aims to model, schedule, optimize, manage and analyse proposed methos, protocols and algorithms for these subjects. Probabilistic models, Markov Decision models, convex/nonconvex optimisation, stochastic geometry, and reinforcement learning are her key research tools.   

Her main research interests include, but not limited to the scalability and coverage of Internet of Things and low power wide area networks via resource management algorithms, stochastic geometry-based analysis, spatiotemporal modelling, and machine learning and reinforcement learning algorithms. 

To date, her research interests can be categorized under various themes:  

  • Performance limits in the low power regime: ergodic capacity in Rician fading, Nakagami fading, Gamma-Gamma fading, MIMO system, Log-Normal fading with possibly 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 
  • Reinforcement learning and deep learning: Freshness of information in relay systems and resource allocation for energy efficient LoRa networks.
  • Physical layer security: lightweight pairwise key generation schemes in LoRa networks, and robust group key generation schemes for asset management applications.

Key research areas: Wireless communications, Internet of Things, wireless power transfer, age of information, physical layer security, optimization and machine learning, stochastic geometry. 


For self-motivated students who want to pursue a PhD degree in [Electronic Engineering and Computer science] and have solid background in wireless communications with good mathematical skills, please contact me by email and send your CV and draft research proposal. More information on the application process and funding opportunities are available here: PhD fees and funding - School of Electronic Engineering and Computer Science ( 

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