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Found 9 papers, 2 papers with code
Network-Wide Task Offloading With LEO Satellites: A Computation and Transmission Fusion Approach
To address the network-wide offloading problem, we propose a metagraph-based computation and transmission fusion offloading scheme for multi-tier networks.
Adaptive Task Offloading for Space Missions: A State-Graph-Based Approach
However, the overall delay is determined by both computation and transmission costs.
Low-Interception Waveform: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples
This leads to the problem of a malicious third party using a deep learning model to easily recognize the modulation format of the transmitted waveform.
RadioNet: Transformer based Radio Map Prediction Model For Dense Urban Environments
Radio Map Prediction (RMP), aiming at estimating coverage of radio wave, has been widely recognized as an enabling technology for improving radio spectrum efficiency.
Mean Field MARL Based Bandwidth Negotiation Method for Massive Devices Spectrum Sharing
In this paper, a novel bandwidth negotiation mechanism is proposed for massive devices wireless spectrum sharing, in which individual device locally negotiates bandwidth usage with neighbor devices and globally optimal spectrum utilization is achieved through distributed decision-making.
Noise Attention based Spectrum Anomaly Detection Method for Unauthorized Bands
In this paper, a noise attention method is proposed for unsupervised spectrum anomaly detection in unauthorized bands.
Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches
To improve the ability of Ad-YOLO to detect variety patches, we first use an adversarial training process to develop a patch dataset based on the Inria dataset, which we name Inria-Patch.
Blind Adversarial Training: Balance Accuracy and Robustness
The main idea of this approach is to use a cutoff-scale strategy to adaptively estimate a nonuniform budget to modify the AEs used in the training, ensuring that the strengths of the AEs are dynamically located in a reasonable range and ultimately improving the overall robustness of the AT model.
Blind Adversarial Pruning: Balance Accuracy, Efficiency and Robustness
Furthermore, to better balance the AER, we propose an approach called blind adversarial pruning (BAP), which introduces the idea of blind adversarial training into the gradual pruning process.
