ai-driven optimization for enhanced space communications and navigation systems
- Paper ID
102741
- DOI
- author
- company
Azerbaijan State Oil and Industry University (SABAH Groups); Azerbaijan State Oil and Industry University (ASOIU); Institute of Physics of the Ministry of Science and Education of the Republic of Azerbaijan
- country
Azerbaijan
- year
2025
- abstract
The growing dependence on satellite communication and navigation systems offers various opportunities and challenges in terms of global connectivity and space exploration beyond Earth's atmosphere. Conventional signal processing methods are unable to cope with the requirements of processing large amounts of data while maintaining steady real-time data transmission. In this study, we discuss the contribution of artificial intelligence in space communication networks focusing on its potential to enhance signal processing, optimize the utilization of bandwidth, and improve the precision of navigation systems in extraterrestrial space. Current advances in modulation recognition facilitated by artificial intelligence and adaptive signal processing have been shown to bring dramatic gains in communication network efficiency and robustness. Studies by NASA and the European Space Agency have examined AI-augmented protocols optimized for deep-space missions, particularly missions to Mars, where signal degradation and latency issues are of utmost concern. The present research contributes to the literature by including a machine learning-based system for dynamic spectrum allocation and error correction in interplanetary communication channels. The method here uses deep learning models for analyzing and predicting signal disruptions due to terrestrial causes, space weather phenomena, and terrestrial interference. Through the integration of artificial intelligence-driven models into satellite networks already in place, the proposed system significantly improves real-time decision-making in space communication. Simulation results indicate the potential to improve the efficiency of data transmission by up to 35\% with reduced latency and improved signal fidelity over long-haul communications. This research underscores the central place of artificial intelligence in the advancement of space communication and navigation in the future. By automating tasks such as signal classification, network optimization, and adaptive error correction, AI has the prospects of transforming the efficiency of future and current space missions. The findings provide a strategic roadmap to the integration of AI in the satellite networks of the future, hence enabling the realization of more robust and scalable communication systems to support deep-space exploration and worldwide connectivity.