Utilizing Machine Learning for Signal Classification and Noise Reduction in Amateur Radio

arXiv:2402.17771v1 Announce Type: cross
Abstract: In the realm of amateur radio, the effective classification of signals and the mitigation of noise play crucial roles in ensuring reliable communication. Traditional methods for signal classification and noise reduction often rely on manual intervention and predefined thresholds, which can be labor-intensive and less adaptable to dynamic radio environments. In this paper, we explore the application of machine learning techniques for signal classification and noise reduction in amateur radio operations. We investigate the feasibility …

abstract arxiv classification communication cs.lg eess.sp labor machine machine learning noise radio roles signal type