Improving Data Transmission In Satellite Network Using Intelligent Based Beam Forming Technique

Abstract

Efficient data transmission in satellite networks is crucial for ensuring seamless communication, high-speed connectivity, and minimal latency. However, conventional satellite communication systems often suffer from issues such as signal degradation, interference, and limited bandwidth efficiency. This research focuses on improving data transmission in satellite networks using an intelligent-based beam forming technique. The proposed approach leverages artificial intelligence (AI) and machine learning (ML) algorithms to dynamically optimize beam forming patterns, enhance signal strength, and reduce interference in real time. By intelligently directing satellite beams towards intended receivers while mitigating signal loss, the system significantly improves data throughput and network reliability. The study further explores the integration of deep learning models to predict and adapt to changing network conditions, ensuring optimal performance under various atmospheric and operational challenges. Simulation results demonstrate that the intelligent-based beam forming technique outperforms traditional methods in terms of spectral efficiency, reduced bit error rate (BER), and enhanced overall network performance. This research contributes to the advancement of satellite communication technology, enabling more robust and efficient data transmission for applications in telecommunications, remote sensing, and global connectivity.