Abstract
It may be helpful to integrate multiple aircraft communication and navigation functions into a single software-defined radio (SDR) platform. To transmit these multiple signals, the SDR would first sum the baseband version of the signals. This outgoing composite signal would be passed through a digital-to-analog converter (DAC) before being up-converted and passed through a radio frequency (RF) amplifier. To prevent non-linear distortion in the RF amplifier, it is important to know the peak voltage of the composite. While this is reasonably straightforward when a single modulation is used, it is more challenging when working with composite signals. This paper describes a machine learning solution to this problem. We demonstrate that a generalized gamma distribution (GGD) is a good fit for the distribution of the instantaneous voltage of the composite waveform. A deep neural network was trained to estimate the GGD parameters based on the parameters of the modulators. This allows the SDR to accurately estimate the peak of the composite voltage and set the gain of the DAC and RF amplifier, without having to generate or directly observe the composite signal.
Recommended Citation
V. K. Gajjar and K. L. Kosbar, "Deep Learning-Based Gain Estimation for Multi-User Software-Defined Radios in Aircraft Communications," Signals, vol. 6, no. 1, article no. 3, MDPI, Mar 2025.
The definitive version is available at https://doi.org/10.3390/signals6010003
Department(s)
Electrical and Computer Engineering
Publication Status
Open Access
Keywords and Phrases
aircraft communication; deep learning; digital-to-analog converter; gain estimation; signal processing; software-defined radio
International Standard Serial Number (ISSN)
2624-6120
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2025 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Mar 2025
