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- Article name
- A NEURAL NETWORK METHOD FOR RESTORING THE RECURRENT LAW OF PSEUDORANDOM ADJUSTMENT OF THE OPERATING FREQUENCY OF SIGNALS BASED ON BILSTM WITH A SELF-AWARENESS MECHANISM
- Authors
- Savin D. A., , denissatelit@yandex.ru, MIREA - Russian Technological University, Moscow, Russia
- Keywords
- frequency-hopping spread spectrum / FHSS / radio monitoring / recurrent neural networks / BiLSTM / self-attention mechanism / Self-Attention / Bluetooth
- Year
- 2026 Issue 3 Pages 16 - 20
- Code EDN
- AHFALG
- Code DOI
- 10.52190/1729-6552_2026_3_16
- Abstract
- The article presents a neural network method for recovering the recurrent law of pseudo-random frequency hopping (FH) signals. A model based on a bidirectional recurrent neural network with a self-attention mechanism (BiLSTM + Self-Attention) is developed. The model solves the problem of predicting the next frequency channel based on a sequence of channel indices of length 10. Synthetic sequences generated according to three different recurrent rules simulating Bluetooth systems were used for training and testing. Experimental results demonstrated high prediction accuracy. The model showed good robustness to noise and errors in the frequency channel sequence. The proposed approach makes it possible to move from traditional parametric methods to intelligent analysis of FH signals under conditions of a priori uncertainty and interference. The results can be used in automated radio monitoring systems for wireless technologies.
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