Deep Learning-Based Gain Estimation for Multi-User Software-Defined Radios in Aircraft Communications
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 digit...
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MDPI AG
2025-01-01
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| Series: | Signals |
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| Online Access: | https://www.mdpi.com/2624-6120/6/1/3 |
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| author | Viraj K. Gajjar Kurt L. Kosbar |
| author_facet | Viraj K. Gajjar Kurt L. Kosbar |
| author_sort | Viraj K. Gajjar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1dba1c33e4ee404baa2fae90ca1cf7e4 |
| institution | OA Journals |
| issn | 2624-6120 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Signals |
| spelling | doaj-art-1dba1c33e4ee404baa2fae90ca1cf7e42025-08-20T01:49:03ZengMDPI AGSignals2624-61202025-01-0161310.3390/signals6010003Deep Learning-Based Gain Estimation for Multi-User Software-Defined Radios in Aircraft CommunicationsViraj K. Gajjar0Kurt L. Kosbar1Electrical and Computer Engineering Department, Missouri University of Science and Technology, Rolla, MO 65409, USAElectrical and Computer Engineering Department, Missouri University of Science and Technology, Rolla, MO 65409, USAIt 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.https://www.mdpi.com/2624-6120/6/1/3gain estimationdigital-to-analog convertersoftware-defined radioaircraft communicationsignal processingdeep learning |
| spellingShingle | Viraj K. Gajjar Kurt L. Kosbar Deep Learning-Based Gain Estimation for Multi-User Software-Defined Radios in Aircraft Communications Signals gain estimation digital-to-analog converter software-defined radio aircraft communication signal processing deep learning |
| title | Deep Learning-Based Gain Estimation for Multi-User Software-Defined Radios in Aircraft Communications |
| title_full | Deep Learning-Based Gain Estimation for Multi-User Software-Defined Radios in Aircraft Communications |
| title_fullStr | Deep Learning-Based Gain Estimation for Multi-User Software-Defined Radios in Aircraft Communications |
| title_full_unstemmed | Deep Learning-Based Gain Estimation for Multi-User Software-Defined Radios in Aircraft Communications |
| title_short | Deep Learning-Based Gain Estimation for Multi-User Software-Defined Radios in Aircraft Communications |
| title_sort | deep learning based gain estimation for multi user software defined radios in aircraft communications |
| topic | gain estimation digital-to-analog converter software-defined radio aircraft communication signal processing deep learning |
| url | https://www.mdpi.com/2624-6120/6/1/3 |
| work_keys_str_mv | AT virajkgajjar deeplearningbasedgainestimationformultiusersoftwaredefinedradiosinaircraftcommunications AT kurtlkosbar deeplearningbasedgainestimationformultiusersoftwaredefinedradiosinaircraftcommunications |