Multi-task neural network for solving the problem of recognizing the type of QAM and PSK modulation under parametric a priori uncertainty
Objectives. Automatic modulation recognition of unknown signals is an important task for various fields oftechnology such as radio control, radio monitoring, and identification of interference and sources of radio emission. The paper aims to develop a method for recognizing the types of signal modul...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | Russian |
| Published: |
MIREA - Russian Technological University
2023-08-01
|
| Series: | Российский технологический журнал |
| Subjects: | |
| Online Access: | https://www.rtj-mirea.ru/jour/article/view/734 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839584687343796224 |
|---|---|
| author | A. A. Paramonov V. M. Nguyen M. T. Nguyen |
| author_facet | A. A. Paramonov V. M. Nguyen M. T. Nguyen |
| author_sort | A. A. Paramonov |
| collection | DOAJ |
| description | Objectives. Automatic modulation recognition of unknown signals is an important task for various fields oftechnology such as radio control, radio monitoring, and identification of interference and sources of radio emission. The paper aims to develop a method for recognizing the types of signal modulation under conditions of parametric a priori uncertainty, including the uncertainty of carrier frequency- and initial signal phase values. An additional task consists in estimating the offset values of the carrier frequency or signal phase at the initial stage of the recognition process.Methods. A multi-task learning with artificial neural network and the theory of cumulants of random variables are used.Results. For signals with a carrier frequency and initial phase shift, cumulant approaches for QAM-8, APSK-16, QAM-64, and PSK-8 modulations are calculated. A multi-task learning with artificial neural network using cumulant features and a data standardization algorithm is presented. The results of the experiment show that using multi-task learning with an artificial neural network provides high accuracy of recognizing QAM-8 and APSK-16, QAM-64 and PSK-8 modulations with small mismatches of the carrier frequency or initial phase. The accuracy of determining the offset values from the carrier frequency or the initial phase for QAM-8, APSK-16, QAM-64, and PSK-8 modulation is high.Conclusions. The multi-task learning with neural network using high-order signal cumulants makes it possible not only to recognize modulation types with high accuracy under conditions of a priori uncertainty of signal parameters, but also to determine the offset values of carrier frequency or initial signal phase from expected values. |
| format | Article |
| id | doaj-art-96cde943dcea4a89aae40f9437b47a8c |
| institution | Matheson Library |
| issn | 2782-3210 2500-316X |
| language | Russian |
| publishDate | 2023-08-01 |
| publisher | MIREA - Russian Technological University |
| record_format | Article |
| series | Российский технологический журнал |
| spelling | doaj-art-96cde943dcea4a89aae40f9437b47a8c2025-08-03T19:48:04ZrusMIREA - Russian Technological UniversityРоссийский технологический журнал2782-32102500-316X2023-08-01114495810.32362/2500-316X-2023-11-4-49-58382Multi-task neural network for solving the problem of recognizing the type of QAM and PSK modulation under parametric a priori uncertaintyA. A. Paramonov0V. M. Nguyen1M. T. Nguyen2MIREA – Russian Technological UniversityMIREA – Russian Technological UniversityMIREA – Russian Technological UniversityObjectives. Automatic modulation recognition of unknown signals is an important task for various fields oftechnology such as radio control, radio monitoring, and identification of interference and sources of radio emission. The paper aims to develop a method for recognizing the types of signal modulation under conditions of parametric a priori uncertainty, including the uncertainty of carrier frequency- and initial signal phase values. An additional task consists in estimating the offset values of the carrier frequency or signal phase at the initial stage of the recognition process.Methods. A multi-task learning with artificial neural network and the theory of cumulants of random variables are used.Results. For signals with a carrier frequency and initial phase shift, cumulant approaches for QAM-8, APSK-16, QAM-64, and PSK-8 modulations are calculated. A multi-task learning with artificial neural network using cumulant features and a data standardization algorithm is presented. The results of the experiment show that using multi-task learning with an artificial neural network provides high accuracy of recognizing QAM-8 and APSK-16, QAM-64 and PSK-8 modulations with small mismatches of the carrier frequency or initial phase. The accuracy of determining the offset values from the carrier frequency or the initial phase for QAM-8, APSK-16, QAM-64, and PSK-8 modulation is high.Conclusions. The multi-task learning with neural network using high-order signal cumulants makes it possible not only to recognize modulation types with high accuracy under conditions of a priori uncertainty of signal parameters, but also to determine the offset values of carrier frequency or initial signal phase from expected values.https://www.rtj-mirea.ru/jour/article/view/734recognitionneural networkcarrier frequencyinitial phasecumulant feature |
| spellingShingle | A. A. Paramonov V. M. Nguyen M. T. Nguyen Multi-task neural network for solving the problem of recognizing the type of QAM and PSK modulation under parametric a priori uncertainty Российский технологический журнал recognition neural network carrier frequency initial phase cumulant feature |
| title | Multi-task neural network for solving the problem of recognizing the type of QAM and PSK modulation under parametric a priori uncertainty |
| title_full | Multi-task neural network for solving the problem of recognizing the type of QAM and PSK modulation under parametric a priori uncertainty |
| title_fullStr | Multi-task neural network for solving the problem of recognizing the type of QAM and PSK modulation under parametric a priori uncertainty |
| title_full_unstemmed | Multi-task neural network for solving the problem of recognizing the type of QAM and PSK modulation under parametric a priori uncertainty |
| title_short | Multi-task neural network for solving the problem of recognizing the type of QAM and PSK modulation under parametric a priori uncertainty |
| title_sort | multi task neural network for solving the problem of recognizing the type of qam and psk modulation under parametric a priori uncertainty |
| topic | recognition neural network carrier frequency initial phase cumulant feature |
| url | https://www.rtj-mirea.ru/jour/article/view/734 |
| work_keys_str_mv | AT aaparamonov multitaskneuralnetworkforsolvingtheproblemofrecognizingthetypeofqamandpskmodulationunderparametricaprioriuncertainty AT vmnguyen multitaskneuralnetworkforsolvingtheproblemofrecognizingthetypeofqamandpskmodulationunderparametricaprioriuncertainty AT mtnguyen multitaskneuralnetworkforsolvingtheproblemofrecognizingthetypeofqamandpskmodulationunderparametricaprioriuncertainty |