Improving Rectenna Conversion Efficiency: A Machine Learning Approach


  • William Joseph , Cordelia Ailani Department of Computer Science, University of Harvard


RF Energy Harvesting, Rectenna Conversion Efficiency, Machine Learning Optimization, Data-Driven Design, Energy Harvesting Systems, Antenna-Rectifier Integration, Feature Engineering, Supervised Learning, Optimization Algorithms, Electromagnetic Wave Harvesting


This research explores an innovative approach to enhance the conversion efficiency of rectennas in RF (Radio Frequency) energy harvesting systems through the application of machine learning techniques. The study investigates the use of machine learning algorithms to optimize rectenna circuit parameters, mitigate losses, and improve overall energy conversion efficiency. By leveraging data-driven insights, the proposed approach aims to address challenges associated with traditional rectenna designs, offering a promising avenue for advancing the performance of RF energy harvesting systems.