Deep Learning-Driven Beamforming Optimization for High-Performance 5G Planar Antenna Arrays

The ability of 5G wireless communication networks to effectively and simultaneously interact with incoming signals is made possible by antenna arrays, which play a vital role in assisting the functioning of 5G wireless communication networks. The utilization of beamforming enables the enhancement of signal strength, expansion of coverage area, and reduction of interference, thereby optimizing the performance of the communication networks. This paper introduces a deep learning approach that utilizes a deep neural network (DNN). This approach establishes an appropriate framework to implement beamforming for planar antenna arrays. The DNN utilizes the desired radiation pattern as an input to generate the complex excitation coefficients for each antenna element. For the purpose of enhancing the training procedure of the DNN being studied, a dataset including 300,000 varied radiation patterns was developed. These patterns were created by changing the amplitude and phase of each element within a uniform planar array by 208 elements. To showcase the efficacy of our proposed methodology, we conducted simulations in two different beamforming scenarios, namely single-beam and multi-beam modes. The simulations demonstrate that the utilization of beamforming techniques on the antennas within the novel approach has the potential to enhance the reliability and effectiveness of wireless communication networks in dynamic mode, as well as other antenna array systems.