Low-complexity multi-antenna RF wireless power transfer: Signal processing and system design
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
Wetteri-sali (IT115), Linnanmaa
Topic of the dissertation
Low-complexity multi-antenna RF wireless power transfer: Signal processing and system design
Doctoral candidate
Master of Science (Technology) Amirhossein Azarbahram
Faculty and unit
University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, CWC - Radio Technologies
Subject of study
Communications Engineering
Opponent
Professor Ioannis Krikidis, University of Cyprus
Custos
Associate Professor Onel Luis Alcaraz Lopez, University of Oulu
Efficient multi-antenna RF wireless charging with low-complexity design
The rapid growth of wireless devices and the Internet of Things (IoT) has increased the demand for scalable solutions for delivering energy without physical connections. Radio frequency (RF) wireless power transfer (WPT) enables simultaneous energy delivery to multiple devices over a shared wireless channel, but its efficiency is limited by propagation losses, hardware constraints, and the need to fairly distribute power among receivers under heterogeneous conditions. This thesis addresses these challenges through a systematic study of RF-WPT system design and optimization, covering distributed deployments, metasurface-based architectures, and adaptive charging protocols.
The thesis first investigates hotspot-centric deployments, where energy demand is spatially concentrated. Radio stripes are introduced as distributed transmitters, and their placement and beamforming are optimized to maximize the minimum received power. A clustering-based deployment framework is developed, along with multiple radio stripe configurations ranging from low-complexity structured layouts to free-form cable geometries. The results demonstrate significant performance gains over conventional co-located antenna systems and improved adaptability to varying operating conditions.
To reduce transmitter power consumption, dynamic metasurface antennas (DMA) are integrated into RF-WPT systems. Their tunable architecture enables efficient energy focusing with a limited number of active components. Beamforming and waveform design methods are developed for both linear and non-linear energy harvesting models, showing that DMA-based transmitters can outperform fully digital arrays depending on system configuration and propagation environment.
The thesis then considers beyond-diagonal reconfigurable intelligent surfaces (RIS), which provide enhanced control over the wireless channel. Joint waveform and beamforming optimization algorithms are proposed, revealing that beyond-diagonal RIS can substantially increase harvested energy in rich scattering environments, while highlighting trade-offs relative to diagonal RIS.
Finally, adaptive charging protocols are studied for dynamic and uncertain environments. Reinforcement learning is employed to design power-efficient scheduling strategies under time-varying energy demands. In addition, a sense-then-charge protocol is proposed for scenarios without channel state information, where device locations are estimated through sensing and used for targeted beamforming. Both approaches outperform conventional baseline methods.
Overall, the thesis develops robust, fair, and energy-efficient RF-WPT techniques that support large-scale IoT deployments.
The thesis first investigates hotspot-centric deployments, where energy demand is spatially concentrated. Radio stripes are introduced as distributed transmitters, and their placement and beamforming are optimized to maximize the minimum received power. A clustering-based deployment framework is developed, along with multiple radio stripe configurations ranging from low-complexity structured layouts to free-form cable geometries. The results demonstrate significant performance gains over conventional co-located antenna systems and improved adaptability to varying operating conditions.
To reduce transmitter power consumption, dynamic metasurface antennas (DMA) are integrated into RF-WPT systems. Their tunable architecture enables efficient energy focusing with a limited number of active components. Beamforming and waveform design methods are developed for both linear and non-linear energy harvesting models, showing that DMA-based transmitters can outperform fully digital arrays depending on system configuration and propagation environment.
The thesis then considers beyond-diagonal reconfigurable intelligent surfaces (RIS), which provide enhanced control over the wireless channel. Joint waveform and beamforming optimization algorithms are proposed, revealing that beyond-diagonal RIS can substantially increase harvested energy in rich scattering environments, while highlighting trade-offs relative to diagonal RIS.
Finally, adaptive charging protocols are studied for dynamic and uncertain environments. Reinforcement learning is employed to design power-efficient scheduling strategies under time-varying energy demands. In addition, a sense-then-charge protocol is proposed for scenarios without channel state information, where device locations are estimated through sensing and used for targeted beamforming. Both approaches outperform conventional baseline methods.
Overall, the thesis develops robust, fair, and energy-efficient RF-WPT techniques that support large-scale IoT deployments.
Created 11.2.2026 | Updated 11.2.2026