Automatic Modulation Recognition (AMR) is an important part of spectrum management. Existing work and datasets focus on variety in the modulations transmitted and only apply rudimentary channel effects. We propose a new dataset which supports AMR tasks which focuses on only a few common modulations but introduces a large variation to the propagation channel. Simple scenarios with rural and urban areas are randomly generated using Simplex noise and a receiver/transmitter pair is placed in the scenario. The 3GPP model is combined with the propagation vector from the scenario generator to simulate a signal propagating across the generated terrain. This dataset brings more realism to the AMR task and will allow machine learning models to adapt to changing environments.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.