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

2026-01-15

AI Summary

This study addresses the challenge of achieving efficient, robust, and secure adaptive modulation in dynamic spectrum environments—a longstanding limitation of conventional cognitive radio systems. To this end, the work introduces the Transformer architecture into modulation design for the first time, proposing a GPT-2–based, data-driven approach that automatically generates novel adaptive modulation strategies through training on a dataset of modulation formulations. Experimental results demonstrate that the generated schemes match or even surpass traditional methods in key performance metrics, including signal-to-noise ratio and power spectral density, thereby validating both the efficacy and the novelty of the proposed methodology.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Cognitive Radio
• Adaptive Modulation
• Transformer Models
• Spectral Efficiency
• Wireless Communications

Innovation

• Transformer models
• Cognitive Radio
• Adaptive Modulation
• GPT-2
• Wireless Communications