Utilizing Machine Learning for Signal Classification and Noise Reduction
  in Amateur Radio
Jimi Sanchez
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 and
effectiveness of employing supervised and unsupervised learning algorithms to
automatically differentiate between desired signals and unwanted interference,
as well as to reduce the impact of noise on received transmissions.
Experimental results demonstrate the potential of machine learning approaches
to enhance the efficiency and robustness of amateur radio communication
systems, paving the way for more intelligent and adaptive radio solutions in
the amateur radio community.