Enhancing Data Transmission In Satellite Network Using Ann Based Technique

Abstract

The rapid expansion of global communication systems has placed increasing demands on satellite networks, particularly in terms of data transmission efficiency, reliability, and adaptability. Traditional satellite communication methods often face challenges such as bandwidth limitation, signal attenuation, noise interference, and latency, which hinder optimal data transfer. To address these challenges, this study proposes the application of Artificial Neural Network (ANN) based techniques for enhancing data transmission in satellite networks. The ANN model is designed to learn and adapt to dynamic channel conditions, mitigate the effects of noise and fading, and optimize modulation and coding strategies for improved throughput. Simulation results demonstrate that the ANN-based technique significantly enhances data transmission performance by reducing bit error rates, minimizing packet losses, and achieving higher spectral efficiency compared to conventional methods. The proposed approach also shows superior adaptability to varying atmospheric and network conditions, thereby ensuring reliable communication in real-time scenarios. This study underscores the potential of ANN-based intelligent methods as a robust solution for next-generation satellite communication systems, paving the way for improved connectivity, enhanced bandwidth utilization, and greater system resilience. The results obtained were the conventional packet loss to enhance data transmission in satellite network was3%. On the other hand, when an ANN based technique was integrated into it, it simultaneously reduced it to2.7% and the conventional low carrier to noise to enhance data transmission in satellite network was 8dB. Meanwhile, when an ANN based technique was incorporated into it, it spontaneously increased it to 9.6dB. Finally, the percentage enhancement of data transmission in satellite network when an ANN based technique was imbibed into the system was25%.