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Intelligent Fast Cell Association Scheme Based on Deep Q-Learning in Ultra-Dense Cellular Networks

China Communications 2021年第2期18卷 259-270页

作者： Jinhua Pan Lusheng Wang Hai Lin Zhiheng Zha Caihong Kai Key Laboratory of Knowledge Engineering with Big Data Ministry of Education.School of Computer Science and Information EngineeringHefei University of TechnologyHefei 230601China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei 230601China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education.School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

To support dramatically increased traffic loads,communication networks become *** cell association(CA)schemes are timeconsuming,forcing researchers to seek fast *** paper proposes a deep Q-learning based scheme,whose main idea is to train a deep neural network(DNN)to calculate the Q values of all the state-action pairs and the cell holding the maximum Q value is *** the training stage,the intelligent agent continuously generates samples through the trial-anderror method to train the DNN until *** the application stage,state vectors of all the users are inputted to the trained DNN to quickly obtain a satisfied CA result of a scenario with the same BS locations and user *** demonstrate that the proposed scheme provides satisfied CA results in a computational time several orders of magnitudes shorter than traditional ***,performance metrics,such as capacity and fairness,can be guaranteed.

关键词： ultra-dense cellular networks(UDCN) cell association(CA) deep Q-learning proportional fairness Q-learning

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Passband-Baseband Joint Equalization for Bio-Inspired Phase-Modulated Communication in Underwater IoT Networks

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IEEE Internet of Things Journal 2025年

作者： Liu, Songzuo Qiao, Zhiwen Ma, Tianlong Yang, Yang Shang, Zhigang Harbin Engineering University National Key Laboratory of Underwater Acoustic Technology Key Laboratory of Marine Information Acquisition and Security (Ministry of Industry and Information Technology) Harbin Engineering University College of Underwater Acoustic Engineering Harbin 150001 China Sanya Nanhai Innovation and Development Base of Harbin Engineering University Sanya 572024 China

The Internet of Underwater Things (IoUT) has emerged as a promising paradigm for marine applications, yet faces significant challenges in achieving secure and reliable network communications. In IoUT networks, underwater acoustic communication struggles to maintain both covert operation and efficient data transmission, which are crucial for sensitive monitoring and surveillance applications. Bio-inspired communication schemes that mimic dolphin whistles offer an innovative solution for IoUT networks by embedding information within natural marine sounds, enhancing both communication security and network connectivity. However, in phase-modulated bionic camouflage communication, the multi-path effects of the underwater acoustic channel cause overlapping of multiple whistles at the receiver, severely degrading IoUT network performance and limiting practical applications. To address these challenges in IoUT networks, we propose a novel joint passband-baseband equalization method for bio-inspired phase-modulated communication. The receiver architecture combines an improved learning rate-fast transversal RLS (LR-FTRLS) equalizer for channel shortening in the passband with a decision feedback equalizer (DFE) in the baseband, specifically optimized for underwater IoT scenarios. The effectiveness of the proposed scheme has been verified through both shallow and deep-sea simulations. Sea experiment further demonstrate the system’s practical viability, achieving 300 bps with a bit error rate below 10^-3 at 9 km range, demonstrating its effectiveness for high-rate covert underwater communication. This work represents a significant advancement in underwater IoT networks, offering enhanced security, improved reliability, and increased data transmission capabilities for various marine IoT applications including environmental monitoring, underwater surveillance, and marine resource exploration. © 2014 IEEE.

关键词： Bio-inspired underwater acoustic communication Coherent BPSK modulation Decision feedback equalization (DFE) Dolphin whistle LR-FTRLS equalization

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IntML-KNN: A Few-Shot Radio Frequency Fingerprint Identification Scheme for Lora Devices

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IEEE Signal Processing Letters 2025年

作者： Cao, Xin Tan, Weijie Gao, Qiangqiang Hu, Zhilong Li, Chunguo Guizhou University College of Computer Science and Technology Guiyang 550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang 550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu 610025 China Ministry of Industry and Information Technology Laboratory of Cryptograph Application Technology Innovation and Testing Verification Beijing 100015 China Southeast University School of Information Science and Engineering Nanjing 210096 China

Deep learning (DL) is widely used in radio frequency fingerprint identification (RFFI). However, in few-shot case, traditional DL-based RFFI need to construct auxiliary dataset to realize radio frequency fingerprint identification. To address this issue, we propose a few-shot RFFI (FS-RFFI) method based on interpolating metric learning and KNN (IntML-KNN). Specifically, the method first extends the dataset with data augmentation, and CutMix interpolation. Secondly, combining with metric learning to enhance the generalization capacity of the model. Finally, KNN algorithm is designed to realize device classification and detection. The proposed IntML-KNN method is verified on the commercial available LoRa dataset. The experimental results indicate that the proposed scheme exhibits strong classification and generalization performance in FS-RFFI. Meanwhile, the recognition rate of the proposed IntML-KNN reaches 97.00% with only 10% samples. © 1994-2012 IEEE.

关键词： data augmentation deep learning few-shot LoRa metric learning radio frequency fingerprint

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Vertical Federated Learning Based on Consortium blockchain for data Sharing in Mobile Edge Computing

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 345-361页

作者： Yonghao Zhang Yongtang Wu Tao Li Hui Zhou Yuling Chen State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and TechnologyWeifang262700China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin541004China

The data in Mobile Edge Computing(MEC)contains tremendousmarket value,and data sharing canmaximize the usefulness of the ***,certain data is quite sensitive,and sharing it directly may violate *** Federated Learning(VFL)is a secure distributed machine learning framework that completes joint model training by passing encryptedmodel parameters rather than raw data,so there is no data privacy leakage during the training ***,the VFL can build a bridge between data demander and owner to realize data sharing while protecting data ***,the VFL requires a third party for key distribution and decryption of training *** this article,we employ the consortium blockchain instead of the traditional third party and design a VFL architecture based on the consortium blockchain for data sharing in *** specifically,we propose a V-Raft consensus algorithm based on Verifiable Random Functions(VRFs),which is a variant of the *** VRaft is able to elect leader quickly and stably to assist data demander and owner to complete data sharing by ***,we apply secret sharing todistribute the private key to avoid the situationwhere the training result cannot be decrypted if the leader ***,we analyzed the performance of the V-Raft and carried out simulation experiments,and the results show that compared with Raft,the V-Raft has higher efficiency and better scalability.

关键词： Mobile edge computing vertical federated learning consortium blockchain consensus algorithm

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Cross-modality information Check for Detecting Jailbreaking in Multimodal Large Language Models

Cross-modality Information Check for Detecting Jailbreaking ...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Xu, Yue Qi, Xiuyuan Qin, Zhan Wang, Wenjie School of Information Science and Technology ShanghaiTech University China The State Key Laboratory of Blockchain and Data Security Zhejiang University China

ISBN: (纸本)9798891761681

Multimodal Large Language Models (MLLMs) extend the capacity of LLMs to understand multimodal information comprehensively, achieving remarkable performance in many vision-centric tasks. Despite that, recent studies have shown that these models are susceptible to jailbreak attacks, which refer to an exploitative technique where malicious users can break the safety alignment of the target model and generate misleading and harmful answers. This potential threat is caused by both the inherent vulnerabilities of LLM and the larger attack scope introduced by vision input. To enhance the security of MLLMs against jailbreak attacks, researchers have developed various defense techniques. However, these methods either require modifications to the model's internal structure or demand significant computational resources during the inference phase. Multimodal information is a double-edged sword. While it increases the risk of attacks, it also provides additional data that can enhance safeguards. Inspired by this, we propose Cross-modality information DEtectoR (CIDER), a plug-and-play jailbreaking detector designed to identify maliciously perturbed image inputs, utilizing the cross-modal similarity between harmful queries and adversarial images. CIDER is independent of the target MLLMs and requires less computation cost. Extensive experimental results demonstrate the effectiveness and efficiency of CIDER, as well as its transferability to both white-box and black-box MLLMs. The resource is available at https://***/PandragonXIII/CIDER. © 2024 Association for Computational Linguistics.

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Energy leakage in OFDM sparse channel estimation:The drawback of OMP and the application of image deblurring

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Digital Communications and Networks 2024年第5期10卷 1280-1288页

作者： Gang Qiao Xizhu Qiang Lei Wan Hanbo Jia Acoustic Science and Technology Laboratory Harbin Engineering UniversityHarbin 150001China Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University) Ministry of Industry and Information TechnologyHarbin 150001China College of Underwater Acoustic Engineering Harbin Engineering UniversityHarbin 150001China School of Informatics Xiamen UniversityXiamen 361005China Key Laboratory of Underwater Acoustic Communication and Marine Information Technology(Xiamen University) Ministry of EducationXiamen 361005China

In this paper,in order to reduce the energy leakage caused by the discretized representation in sparse channel estimation for Orthogonal Frequency Division Multiplexing(OFDM)systems,we systematically have analyzed the optimal locations of atoms with discrete delays for each path reconstruction from the perspective of linear fitting ***,we have investigated the adverse effects of the non-ideal inner product function on the iteration in one of the most widely used channel estimation method,Orthogonal Matching Pursuit(OMP).The study shows that the distance between the selected atoms for each path in OMP can be larger than the sampling interval,which prevents OMP-based methods from achieving better *** overcome this drawback,the image deblurring-based channel estimation method,in which the channel estimation problem is analogized to one-dimensional image deblurring,was proposed to improve the large compensation distance of traditional *** advantage of the proposed method was validated by the results of numerical simulation and sea trial data decoding.

关键词： Sparse channel estimation Orthogonal matching pursuit(OMP) Orthogonal frequency division multiplexing (OFDM) Linear fitting Image deblurring

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Experimental Comparison of Graph Edit Distance Computation Methods 24

Experimental Comparison of Graph Edit Distance Computation M...

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24th IEEE International Conference on Mobile data Management, MDM 2023

作者： Zhang, Gaoming Lin, Sen Wang, Xianmin Huang, Teng Hu, Xuan Zou, Lingyun Guangzhou University Institute of Artificial Intelligence and Blockchain China Ceprei Laboratory Information Security Research Center China Key Laboratory of Ministry of Industry and Information Technology China CSSC Systems Engineering Research Institute China

ISBN: (纸本)9798350341010

Graph edit distance (GED) is a fundamental graph similarity metric. GED computation is NP-hard [10], and exact GED computation is only feasible for small graphs. Therefore, many methods of approximate GED computation have been proposed in the literature. In this paper, we select the five representative GED approximation methods and compare their performance on two real-world datasets. We observe that non-heuristic algorithms such as LSa [1] are fast and accurate in computing true GED for small graphs, and heuristic algorithms such as GENN [4] are very effective in computing the estimated path cost. This effort helps us pinpoint suitable algorithms for different applications. © 2023 IEEE.

关键词： Heuristic algorithms

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GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio Synthesis 24

GROOT: Generating Robust Watermark for Diffusion-Model-Based...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Weizhi Li, Yue Lin, Dongdong Tian, Hui Li, Haizhou Huaqiao University Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen China

ISBN: (纸本)9798400706868

Amid the burgeoning development of generative models like diffusion models, the task of differentiating synthesized audio from its natural counterpart grows more daunting. Deepfake detection offers a viable solution to combat this challenge. Yet, this defensive measure unintentionally fuels the continued refinement of generative models. Watermarking emerges as a proactive and sustainable tactic, preemptively regulating the creation and dissemination of synthesized content. Thus, this paper, as a pioneer, proposes the generative robust audiowatermarking method (Groot), presenting a paradigm for proactively supervising the synthesized audio and its source diffusion models. In this paradigm, the processes of watermark generation and audio synthesis occur simultaneously, facilitated by parameter-fixed diffusion models equipped with a dedicated encoder. The watermark embedded within the audio can subsequently be retrieved by a lightweight decoder. The experimental results highlight Groot's outstanding performance, particularly in terms of robustness, surpassing that of the leading state-of-the-art methods. Beyond its impressive resilience against individual post-processing attacks, Groot exhibits exceptional robustness when facing compound attacks, maintaining an average watermark extraction accuracy of around 95%. Our audio samples are available at https://***/. © 2024 ACM.

关键词： Audio watermarking

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Distributed privacy-preserving keyword querying for integrated data in IoT networks via function secret sharing

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information Fusion 2025年 123卷

作者： Wei Shao Lianhai Wang Chunfu Jia Qizheng Wang Jinpeng Wang Shujiang Xu Shuhui Zhang Mingyue Li Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 Shandong China Key Laboratory of Data and Intelligent System Security Ministry of Education Nankai University Tianjin 300350 China School of Cyber Sciences Hebei University Baoding Hebei China

The growing adoption of IoT applications underscores the need for advanced data fusion and information acquisition techniques, driving demand for secure, privacy-preserving querying of integrated IoT data. Existing schemes like searchable encryption are practical but leak access patterns, while leakage-free methods using Oblivious RAM or cryptographic techniques incur significant resource overhead. In this paper, we propose PQBL, a framework for privacy-preserving, trusted data integration and search, leveraging distributed trust against malicious attackers. Our query scheme combines function secret sharing and blockchain to enable efficient, privacy-preserving searches on encrypted IoT data. To improve search efficiency, we introduce a compressed RAMBO Bloom Filter for keyword trapdoors. Formal security analysis shows that PQBL leaks no search patterns and is secure against Privacy under Selective Chosen-Plaintext Attacks. Extensive experiments on the PQBL prototype validate its effectiveness and efficiency.

关键词： Privacy by design

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High-Resolution Quaternion-Based Algorithm for Coherent Underwater Sources with Linear Vector-Hydrophone Array

High-Resolution Quaternion-Based Algorithm for Coherent Unde...

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2021 OES China Ocean Acoustics, COA 2021

作者： Lou, Yi Qu, Xinghao Sun, Ruofan Qiao, Gang Harbin Engineering University Key Laboratory of Marine Information Acquisition and Security Ministry of Industry and Information Technology College of Underwater Acoustic Engineering Acoustic Science and Technology Laboratory Harbin China

ISBN: (纸本)9781728181905

For scenarios of coherent underwater signals at a low signal-to-noise ratio (SNR), a novel high-resolution DOA algorithm based on quaternion algebra has been pro-posed to improve the poor performance of the subspace-based method without eigendecomposition (SUMWE). We construct four quaternion-based models by judiciously arranging the received data and then obtain the signals' statistical characteristics by performing cross-correlations between the models. In the quaternion algebra framework, we statistically eliminate the autocorrelation noise by using the properties of circular signals. The noise elimination provides a high-resolution performance for the algorithm at a low SNR. Moreover, both the cross-correlations and autocorrelations are utilized to enhance DOA estimation accuracy. Compared with the SUMWE-like methods, the proposed algorithm shows superiority in terms of estimation accuracy and angular resolution. © 2021 IEEE.

关键词： Signal to noise ratio

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