Deep Learning-Enhanced Beamforming for Signal Quality Improvement in Heterogeneous Wireless Networks

Beamforming is now a basic technique in wireless communication to improve signal quality and reduce interference. This study investigates the use of deep learning-enhanced beamforming to improve the quality of transmissions in heterogeneous wireless networks. Using a deep convolutional neural network (CNN), the suggested approach aims to predict the ideal weight vectors for antenna arrays, thereby overcoming the challenges of different network circumstances and interference kinds. The efficiency of the suggested approach in achieving higher signal-to-simulation results shows the interference-plus-noise ratio (SINR) over conventional beamforming methods.