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Transformer-Based Cognitive Radio: Adaptive Modulation Strategies Using Transformer Models

Andrea Melis, Andrea Piroddi, Roberto Girau

TL;DR

This work tackles the challenge of designing efficient, robust modulation schemes for cognitive radio (CR) by leveraging Transformer-based models. A GPT-2 architecture is fine-tuned on a curated corpus of modulation formulas to generate novel modulation schemes, which are then evaluated under identical conditions using $SNR$, BER, spectral efficiency, and PSD against traditional schemes. The results show that a subset of Transformer-generated modulations can achieve comparable or superior $SNR$ and higher spectral efficiency than 256QAM in some cases, though many generated formulas are syntactically invalid and BER remains a trade-off. Overall, the study demonstrates the potential of Transformer-driven modulation design to enable adaptive, secure, and spectrally efficient CR systems, with implications for automatic modulation classification and dynamic spectrum sensing.

Abstract

Cognitive Radio (CR) systems, which dynamically adapt to changing spectrum environments, could benefit significantly from advancements in machine learning technologies. These systems can be enhanced in terms of spectral efficiency, robustness, and security through innovative approaches such as the use of Transformer models. This work investigates the application of Transformer models, specifically the GPT-2 architecture, to generate novel modulation schemes for wireless communications. By training a GPT-2 model on a dataset of existing modulation formulas, new modulation schemes has been created. These generated schemes are then compared to traditional methods using key performance metrics such as Signal-to-Noise Ratio (SNR) and Power Spectrum Density (PSD). The results show that Transformer-generated modulation schemes can achieve performance comparable to, and in some cases outperforming, traditional methods. This demonstrates that advanced CR systems could greatly benefit from the implementation of Transformer models, leading to more efficient, robust, and secure communication systems.

Transformer-Based Cognitive Radio: Adaptive Modulation Strategies Using Transformer Models

TL;DR

This work tackles the challenge of designing efficient, robust modulation schemes for cognitive radio (CR) by leveraging Transformer-based models. A GPT-2 architecture is fine-tuned on a curated corpus of modulation formulas to generate novel modulation schemes, which are then evaluated under identical conditions using , BER, spectral efficiency, and PSD against traditional schemes. The results show that a subset of Transformer-generated modulations can achieve comparable or superior and higher spectral efficiency than 256QAM in some cases, though many generated formulas are syntactically invalid and BER remains a trade-off. Overall, the study demonstrates the potential of Transformer-driven modulation design to enable adaptive, secure, and spectrally efficient CR systems, with implications for automatic modulation classification and dynamic spectrum sensing.

Abstract

Cognitive Radio (CR) systems, which dynamically adapt to changing spectrum environments, could benefit significantly from advancements in machine learning technologies. These systems can be enhanced in terms of spectral efficiency, robustness, and security through innovative approaches such as the use of Transformer models. This work investigates the application of Transformer models, specifically the GPT-2 architecture, to generate novel modulation schemes for wireless communications. By training a GPT-2 model on a dataset of existing modulation formulas, new modulation schemes has been created. These generated schemes are then compared to traditional methods using key performance metrics such as Signal-to-Noise Ratio (SNR) and Power Spectrum Density (PSD). The results show that Transformer-generated modulation schemes can achieve performance comparable to, and in some cases outperforming, traditional methods. This demonstrates that advanced CR systems could greatly benefit from the implementation of Transformer models, leading to more efficient, robust, and secure communication systems.
Paper Structure (52 sections, 4 equations, 5 figures, 4 tables)

This paper contains 52 sections, 4 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background on cognitive radio
  3. Transformer Models
  4. Related Work
  5. CyberSecurity Applications
  6. Methodology
  7. Transformer Model Architecture
  8. Data Preparation and Preprocessing
  9. Training Data Diversity and Representativeness
  10. Training Procedure and Hyperparameters
  11. Modulation Schemes in the Training Data
  12. Analog Modulations
  13. Digital Modulations
  14. Training Parameters
  15. Formula Generation and Evaluation Process
  16. ...and 37 more sections

Figures (5)

  • Figure 1: Spectrogram of the generated modulation.
  • Figure 2: Spectrogram 16QAM.
  • Figure 3: Constellation diagram Modulation 3.
  • Figure 4: Constellation diagram 16QAM.
  • Figure 5: PSD of 256QAM and the generated ones.