Adaptive Filtering and Machine Learning Approaches to Radio Frequency Interference Mitigation and Bandwidth Optimization in Space Communication Systems

In space communication systems, radio frequency interference (RFI) poses a critical challenge to reliable data transmission, affecting both signal integrity and bandwidth utilization. This research presents a comprehensive approach combining adaptive filtering, machine learning, and bandwidth optimization techniques to effectively identify, classify, and mitigate RFI in space communications. We first model narrowband and broadband interference in simulated signals and employ adaptive Least Mean Squares (LMS) filtering to attenuate unwanted noise. Furthermore, a machine learning classifier is developed to categorize interference types based on key signal features, enabling automated interference type recognition. Finally, an adaptive bandwidth allocation algorithm is proposed, dynamically adjusting bandwidth in response to interference levels to maximize communication efficiency. Results demonstrate that the LMS filter significantly reduces interference, achieving stable signal reconstruction with minimal error, while the machine learning classifier accurately distinguishes between various interference types with high precision. The adaptive bandwidth algorithm successfully allocates resources according to interference conditions, preserving data throughput. This study establishes a robust framework for addressing RFI in space communication and contributes to enhancing data fidelity and operational reliability in extraterrestrial communication networks.