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- Article name
- USING A NEURAL NETWORK TO DETECT THE RECURRENCE RELATION OF A SIGNAL WITH FHSS
- Authors
- Savin D. A., , d.a.savin@mirea-rpt.ru, MIREA - Russian Technological University, Moscow, Russia
- Keywords
- FHSS / radiomonitoring / artificial intelligence / neural networks / recurrence relation / BiLSTM / Self-attention
- Year
- 2025 Issue 3 Pages 13 - 16
- Code EDN
- PWSBCI
- Code DOI
- 10.52190/1729-6552_2025_3_13
- Abstract
- The article discusses the application of neural network methods for analyzing signals with frequency-hopping spread spectrum (FHSS). A method is proposed for recovering the recurrence relation that governs the frequency hopping process from observed channel sequences. A BiLSTM-based model with self-attention mechanism is trained on synthetic datasets with known recurrence rules and demonstrates high accuracy in predicting the next frequency channel. Experimental results, including spectral visualizations, accuracy curves, and attention maps, confirm the model's ability to extract the hidden structure of FHSS signals. The results indicate the potential of artificial intelligence for advanced radiomonitoring and structural analysis of hopping algorithms.
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