Design and optimize of rectangular microstrip patch antenna arrays performance at 2.4 GHz - Ali, Khamis Hassan (Kulliyyah of Engineering, International Islamic University Malaysia, 2018)
The demand for small and reliable, high performance, diverse polarization, low-profile, and lightweight antennas has greatly increased. Its demand is in wireless communication, mobile communications, satellite communication, electronic warfare, biological telemetry and IoT technology. Microstrip patch antennas are examples of low profile antennas. In the current highly demanding consumer world for microstrip patch antenna enabled systems, an effective and efficient higher manufacturing processing capability is required. In this research, an investigation of patch antenna array with beamforming technology was performed in detailed. The aim is to increase the data rate and capacity in Wi-Fi applications making it ready for IoT technology. It is expected that Wi-Fi will be one of the connecting devices in IoT since it is widely used in houses, public and industrial places. The array is composed of rectangular patches with modified slot on Rogers 5880 at 2.4 GHz. There is a challenge while approximating antenna array characteristics which is mutual coupling, that can result inaccurate radiation pattern of whole antenna array. To overcome this, a technique using active element pattern from a full wave antenna software was proposed as a solution to this research. Later, the beamforming technology was performed using a combination of active element pattern and genetic algorithm techniques in order to optimize the performance of the antenna array. Hence, after results analysis, the main beam was able to be steered in the desired direction from angle 10º to 30º. However, from angle 40º to 50º, main beam was not steered to the desired direction due to limitation of linear array which is as angle of steering increasing, mutual coupling increasing. Therefore, more works are needed on lowering mutation rates and increasing crossover rates for Genetic Algorithm optimization process.