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5 results on '"Xu, Ningya"'

1. Can We Improve Channel Reciprocity via Loop-back Compensation for RIS-assisted Physical Layer Key Generation

Author

Xu, Ningya, Nan, Guoshun, Tao, Xiaofeng, Li, Na, Mao, Pengxuan, and Yang, Tianyuan

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Reconfigurable intelligent surface (RIS) facilitates the extraction of unpredictable channel features for physical layer key generation (PKG), securing communications among legitimate users with symmetric keys. Previous works have demonstrated that channel reciprocity plays a crucial role in generating symmetric keys in PKG systems, whereas, in reality, reciprocity is greatly affected by hardware interference and RIS-based jamming attacks. This motivates us to propose LoCKey, a novel approach that aims to improve channel reciprocity by mitigating interferences and attacks with a loop-back compensation scheme, thus maximizing the secrecy performance of the PKG system. Specifically, our proposed LoCKey is capable of effectively compensating for the CSI non-reciprocity by the combination of transmit-back signal value and error minimization module. Firstly, we introduce the entire flowchart of our method and provide an in-depth discussion of each step. Following that, we delve into a theoretical analysis of the performance optimizations when our LoCKey is applied for CSI reciprocity enhancement. Finally, we conduct experiments to verify the effectiveness of the proposed LoCKey in improving channel reciprocity under various interferences for RIS-assisted wireless communications. The results demonstrate a significant improvement in both the rate of key generation assisted by the RIS and the consistency of the generated keys, showing great potential for the practical deployment of our LoCKey in future wireless systems., Comment: Accepted by ICC 2024

Published
2024

3. Passive Eavesdropping Can Significantly Slow Down RIS-Assisted Secret Key Generation

Author

Xu, Ningya, Nan, Guoshun, and Tao, Xiaofeng

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Reconfigurable Intelligent Surface (RIS) assisted physical layer key generation has shown great potential to secure wireless communications by smartly controlling signals such as phase and amplitude. However, previous studies mainly focus on RIS adjustment under ideal conditions, while the correlation between the eavesdropping channel and the legitimate channel, a more practical setting in the real world, is still largely under-explored for the key generation. To fill this gap, this paper aims to maximize the RIS-assisted physical-layer secret key generation by optimizing the RIS units switching under the eavesdropping channel. Firstly, we theoretically show that passive eavesdropping significantly reduces RIS-assisted secret key generation. Keeping this in mind, we then introduce a mathematical formulation to maximize the key generation rate and provide a step-by-step analysis. Extensive experiments show the effectiveness of our method in benefiting the secret key capacity under the eavesdropping channel. We also observe that the key randomness, and unmatched key rate, two metrics that measure the secret key quality, are also significantly improved, potentially paving the way to RIS-assisted key generation in real-world scenarios., Comment: Accepted by Globecom 2023

Published
2023

5. Polymorphic Clustering and Approximate Masking Framework for Fine-Grained Insect Image Classification.

Author

Huo, Hua, Mei, Aokun, and Xu, Ningya

Subjects
IMAGE recognition (Computer vision), CLASSIFICATION of insects, FEATURE extraction, INSECT diversity, INSECT morphology
Abstract

Insect diversity monitoring is crucial for biological pest control in agriculture and forestry. Modern monitoring of insect species relies heavily on fine-grained image classification models. Fine-grained image classification faces challenges such as small inter-class differences and large intra-class variances, which are even more pronounced in insect scenes where insect species often exhibit significant morphological differences across multiple life stages. To address these challenges, we introduce segmentation and clustering operations into the image classification task and design a novel network model training framework for fine-grained classification of insect images using multi-modality clustering and approximate mask methods, named PCAM-Frame. In the first stage of the framework, we adopt the Polymorphic Clustering Module, and segmentation and clustering operations are employed to distinguish various morphologies of insects at different life stages, allowing the model to differentiate between samples at different life stages during training. The second stage consists of a feature extraction network, called Basenet, which can be any mainstream network that performs well in fine-grained image classification tasks, aiming to provide pre-classification confidence for the next stage. In the third stage, we apply the Approximate Masking Module to mask the common attention regions of the most likely classes and continuously adjust the convergence direction of the model during training using a Deviation Loss function. We apply PCAM-Frame with multiple classification networks as the Basenet in the second stage and conduct extensive experiments on the Insecta dataset of iNaturalist 2017 and IP102 dataset, achieving improvements of 2.2% and 1.4%, respectively. Generalization experiments on other fine-grained image classification datasets such as CUB200-2011 and Stanford Dogs also demonstrate positive effects. These experiments validate the pertinence and effectiveness of our framework PCAM-Frame in fine-grained image classification tasks under complex conditions, particularly in insect scenes. [ABSTRACT FROM AUTHOR]

Published
2024
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