Utilizing terrain-generation to derive realistic channel models for automatic modulation recognition

Kenneth L. Witham; Nishanth Marer Prabhu; Aly Sultan; Marius Necsoiu; Chad Spooner; Gunar Schirner

doi:10.1117/12.3013507

7 June 2024 Utilizing terrain-generation to derive realistic channel models for automatic modulation recognition

Kenneth L. Witham, Nishanth Marer Prabhu, Aly Sultan, Marius Necsoiu, Chad Spooner, Gunar Schirner

Proceedings Volume 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II; 130351B (2024) https://doi.org/10.1117/12.3013507
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kenneth L. Witham, Nishanth Marer Prabhu, Aly Sultan, Marius Necsoiu, Chad Spooner, and Gunar Schirner "Utilizing terrain-generation to derive realistic channel models for automatic modulation recognition", Proc. SPIE 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II, 130351B (7 June 2024); https://doi.org/10.1117/12.3013507

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