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Found 7 papers, 0 papers with code
Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning
For that, we develop a novel quantum reinforcement learning (RL) algorithm that can achieve a faster convergence rate with fewer training parameters compared to DRL thanks to the quantum superposition and quantum entanglement principles.
A Hybrid Training-time and Run-time Defense Against Adversarial Attacks in Modulation Classification
Motivated by the superior performance of deep learning in many applications including computer vision and natural language processing, several recent studies have focused on applying deep neural network for devising future generations of wireless networks.
Countermeasures Against Adversarial Examples in Radio Signal Classification
In this letter, we propose for the first time a countermeasure against adversarial examples in modulation classification.
A Low-Cost Multi-Band Waveform Security Framework in Resource-Constrained Communications
In this paper, we propose a multi-carrier based multi-band waveform-defined security (WDS) framework, independent from CSI and RF chains, to defend against AI eavesdropping.
Control-Oriented Deep Space Communications For Unmanned Space Exploration
In this paper, we propose a communication optimization scheme for a specific cooperation system named the "mother-daughter system".
Design and Analysis of SWIPT with Safety Constraints
In this paper, we provide a complete framework for the design and analysis of far-field SWIPT under safety constraints.
Model-Driven Beamforming Neural Networks
We also offer enhancement methods such as training-set augmentation and transfer learning in order to improve the generality of BNNs, accompanied by computer simulation results and testbed results showing the performance of such BNN solutions.
