BPA-MANN-assisted design of an optoelectronics-enhanced fractal patch antenna for energy-efficient multiband wireless communication

The demand for compact, multiband, and energy-efficient antennas has increased significantly with the rapid growth of wireless communication systems, Internet of Things platforms, and sensor-based applications. Conventional microstrip patch antennas are attractive because of their low profile and ease of fabrication; however, they often suffer from limited bandwidth and restricted frequency agility. To address these limitations, this paper presents a Basic Probability Assignment-based Modified Artificial Neural Network (BPA-MANN)-assisted optoelectronics-enhanced fractal patch antenna framework for energy-efficient multiband wireless communication. The proposed antenna employs a fractalized patch geometry to increase the effective electrical length within a compact area, thereby enabling multiple resonant bands and improved impedance characteristics. In addition, a BPA-MANN framework is introduced to support design optimization and adaptive tuning. The antenna is implemented on an FR-4 substrate and analyzed over the 1–6 GHz operating region. Simulated results demonstrate six resonant bands at 1.56, 3.10, 3.54, 4.04, 4.56, and 4.96 GHz, with return loss values below −10 dB and VSWR values less than 2, confirming acceptable multiband performance. The proposed structure also exhibits improved current distribution and enhanced radiation behavior compared with the reference patch. Owing to its compact geometry, multiband response, and improved energy efficiency, the proposed antenna is suitable for low-power wireless applications. These results confirm that the proposed BPA-MANN-assisted fractal antenna framework offers a promising solution for next-generation energy-aware wireless communication systems.