A Further Realization of Binary Genetic Algorithm to Design a Dual Frequency Band Rectenna for Energy Harvesting in 5G Networks - Yahiea Al Naiemy; Aqeel N. Abdulateef; Ahmed Rifaat Hamad; Mohammed Saadi Ismael; Balachandran Ruthramurthy; Taha A. Elwi; Lajos Nagy; Thomas Zwick (Diyala Journal of Engineering Sciences, 2025)
Energy-autonomous systems have evolved in response to the quick deployment of 5G networks and growing presence of Internet of Things (IoT) devices.   One reasonable approach is hybrid RF energy collecting using rectenna designs.   Optimizing antenna performance for multi-band operation is a significant design difficulty.   This article presents a complex rectenna design targeted toward hybrid energy harvesting in 5G networks.   The design maximizes the geometry of a microstrip patch antenna running at 2.4GHz and 5.8GHz using a Binary Genetic Algorithm (BGA) based on Artificial Intelligence (AI). By use of binary representation of the antenna's patch form, the problem becomes combinatorial optimization.   Using Schottky diodes from the Skyworks SMS7630 and Avago HSMS 285B families, the optimized antenna combines a commercial RF rectifier with nine-stage voltage doubler branches.   After 250 generations with a 50,000-population count, the BGA produced antenna designs with widths of 810 MHz (5.14–5.95 GHz) and 200 MHz (2.38–2.58 GHz).   Obtained were return losses of –41 dB at 2.4 GHz and –38 dB at 5.8 GHz along with matching gains of 6.2 dBi and 7.12 dBi.   With input power levels kept at 6 dBm, the rectifier showed maximum conversion efficiencies of 70% at 2.4 GHz and 42% at 5.8 GHz.   Using a 1 kΩ load resistor to provide impedance matching and preserve a power conversion efficiency of 40%, outside testing generated DC output voltages of 92.6 mV and 64 mV.   For ambient RF energy collecting in 5G and IoT devices, the suggested AI-optimized rectenna design shows great efficiency and dependability.