Defending Adversarial Attacks on Deep Learning-Based Power Allocation in Massive MIMO Using Denoising Autoencoders

Keyphrases

• Massive multiple-input multiple-output (mMIMO) 100%
• Power Allocation 100%
• Deep Learning 100%
• Adversarial Attack 100%
• Denoising Autoencoder 100%
• Deep Learning Model 33%
• Massive MIMO Networks 33%
• Adversary 16%
• Publicly Available 16%
• Regression-based Method 16%
• Regression Model 16%
• Threat Model 16%
• Allocation Model 16%
• Autoencoder 16%
• Mitigation Techniques 16%
• Adversarial Perturbation 16%
• Multiple Attacks 16%
• Perturbed Data 16%
• Precoding Schemes 16%
• Multiple Threats 16%
• Computational Overhead 16%
• Low Computational 16%

Computer Science

• Power Allocation 100%
• Adversarial Machine Learning 100%
• Massive MIMO 100%
• Deep Learning Method 100%
• Denoising Autoencoder 100%
• Deep Learning Model 33%
• Threat Model 16%
• precoding 16%
• Autoencoder 16%