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Huanhuan Jia

dblp:277/3357 · DBLP profile ↗
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5ranked-venue papers
1as first author
4since 2021 · last 2025
0000-0001-8057-5477ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021Computer networks · 1 · 1 since 2021Security and privacy · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Physical-layer communications · 77% Cellular and mobile networks · 23%
Network and information security
1 paper
Privacy and data protection · 100%
Artificial intelligence
1 paper
Graph learning · 100%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Physical-layer communications
digital predistortion
0.912025
Linearization of Fully-Connected Hybrid Beamforming Transmitters Using Analytical Multi-Input Models for Millimeter-Wave Communications · IEEE Trans. Commun. 2025
Physical-layer communications › beamforming
hybrid beamforming
0.912025
Linearization of Fully-Connected Hybrid Beamforming Transmitters Using Analytical Multi-Input Models for Millimeter-Wave Communications · IEEE Trans. Commun. 2025
Cellular and mobile networks
millimeter-wave communication
0.912025
Linearization of Fully-Connected Hybrid Beamforming Transmitters Using Analytical Multi-Input Models for Millimeter-Wave Communications · IEEE Trans. Commun. 2025
Physical-layer communications › receiver design › RF impairment compensation
power amplifier linearization
0.912025
Linearization of Fully-Connected Hybrid Beamforming Transmitters Using Analytical Multi-Input Models for Millimeter-Wave Communications · IEEE Trans. Commun. 2025
Privacy and data protection › differential privacy
private aggregation
0.912025
Distributed Private Aggregation in Graph Neural Networks · USENIX Security Symposium 2025
Machine learning › Graph learning
graph neural network
0.312025
Distributed Private Aggregation in Graph Neural Networks · USENIX Security Symposium 2025
Physical-layer communications › MIMO
massive MIMO
0.312025
Linearization of Fully-Connected Hybrid Beamforming Transmitters Using Analytical Multi-Input Models for Millimeter-Wave Communications · IEEE Trans. Commun. 2025

Methods — techniques the papers use, named apart from their topics

secure aggregation · 1.7differential privacy · 1.7digital pre-distortion · 0.9analytical multi-input behavioral model · 0.9
YearPublicationVenuePosition
2025 Distributed Private Aggregation in Graph Neural Networks
Huanhuan Jia, Yuanbo Zhao, Kai Dong 0001, Zhen Ling 0001, Ming Yang 0001, Junzhou Luo, Xinwen Fu
USENIX Security Symposium1
2025 Linearization of Fully-Connected Hybrid Beamforming Transmitters Using Analytical Multi-Input Models for Millimeter-Wave Communications
abstract
In recent years, fully-connected hybrid beamforming (FC-HBF) architecture has aroused widespread interest for millimeter wave (mmWave) massive multi-input multi-output (MIMO) communication systems. However, the FC-HBF structure suffers from significant linearity deterioration, limiting its applications in actual mmWave transmitters. To resolve this issue, an effective digital predistortion (DPD) method utilizing analytical multi-input behavioral models is proposed in this paper for linearizing the FC-HBF system. Based on the nonlinearity analysis and behavioral modeling of the array response, three analytical multi-input models are derived by embedding the priori beamforming information in the predistorter. The complexity of the proposed analytical models is significantly reduced compared to the state-of-the-art. Numerical simulations and experimental measurement are carried out on a$4\times 64$mmWave FC-HBF array and$2\times 16$quasi-test platform respectively to validate the performance of the proposed DPDs against the state-of-the-art DPDs, which show significant linearization abilities to compensate for the nonlinear distortions of beam signals. The proof-of-the-concept validations in this paper indicate that the proposed scheme is fully capable of linearizing an mmWave FC-HBF array.
Xin Liu 0063, Huanhuan Jia, Ziyue Zhao 0003, Chupeng Yi, Ting Feng 0002, Xiaohua Ma 0001, Wenhua Chen 0002, Zhenghe Feng, Fadhel M. Ghannouchi
IEEE Trans. Commun.2
2024 Artistic font generation network combining font style and glyph structure discriminators
Yalin Miao, Huanhuan Jia, Kaixu Tang
Multim. Tools Appl.2
2021 Chinese font migration combining local and global features learning
Yalin Miao, Huanhuan Jia, Kaixu Tang
Pattern Anal. Appl.2
2019 Chinese font translation with improved Wasserstein generative adversarial network
abstract
Nowadays, various fonts are applied in many fields, and the generation of multiple fonts by computer plays an important role in the inheritance, development and innovation of Chinese culture. Aiming at the existing font generation methods, which have some problems such as stroke deletion, artifact and blur, this paper proposes Chinese font translation with improved wasserstein generative adversarial network. The wasserstein distance is used to measure distance and difference between the two distributions, and the gradient penalty mechanism is used instead of the weight clipping. The residual dense blocks with better flexibility are selected as the core component to extract the features fully and enhance the information transmission between network layers. It realizes the style migration between different Chinese fonts. Experiments show that the proposed method has better performance in font generation details, simplifies the font translation process, and improves the fidelity of font generation.
Yalin Miao, Huanhuan Jia, Kaixu Tang, Yichun Ji
ICMV2