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LSTM-based Frequency Hopping Sequence Prediction

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LSTM-based Frequency Hopping Sequence Prediction

International Conference on Wireless communications and Signal Processing (WCSP)

作者： Gao Li Jianliang Xu Weiguo Shen Wei Wang Zitong Liu Guoru Ding Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of CETC Jiaxing China College of Communications Engineering Army Engineering University China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

The continuous change of communication frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication. The core of this communication process is the frequency-hopping (FH) sequence with pseudo-random characteristics, which controls carrier frequency hopping. However, FH sequence is always generated by a certain model and is a kind of time sequence with certain regularity. Long Short-Term Memory (LSTM) neural network in deep learning has been proved to have strong ability to solve time series problems. Therefore, in this paper, we establish LSTM model to implement FH sequence prediction. The simulation results show that LSTM-based scheme can effectively predict frequency point by point based on historical HF frequency data. Further, we achieve frequency interval prediction based on frequency point prediction.

关键词： Predictive models Time-frequency analysis Frequency control Training Time series analysis Logic gates Spread spectrum communication

Identification of important nodes on large-scale Internet based on unsupervised learning 2020

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Identification of important nodes on large-scale Internet ba...

Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Fuyang Fang Daojuan Zhang Chonghua Wang De Zhang Yuanfei Zhang Chao Li Xi Luo Ye Fu Information Science Academy of China Electronics Technology Group Corporation Beijing China State Grid Key Laboratory of Information & Network Security Global Energy Interconnection Research Institute co. Ltd Beijing China China Industrial Control Systems Cyber Emergency Response Team Beijing China Cyber space Institute of Advanced Technology Guangzhou University Guangzhou China

ISBN: (纸本)9781450387828

In recent years, scholars have conducted in-depth researches on the robustness, structural vulnerability, and detection and identification of devices in cyberspace from different perspectives such as complex networks and cyberspace resource mapping. Aiming at the problem of identifying important nodes on a large-scale Internet, a CETCRank algorithm for identifying important Internet nodes based on unsupervised learning is proposed. When the algorithm analyzes the attributes of each cyberspace equipment, it not only considers the graph structure characteristics based on the network topology, but also integrates the threat metric of cyberspace equipment. Based on the hypothesis of the cyber attack model, the effective identification of important nodes in the Internet can be realized by integrating the node attributes into the constructed Markov chain model. Experiments show that the time and space complexity of the CETCRank algorithm is suitable for analyzing large-scale Internet, and the recognition performance of important nodes is better than the PageRank algorithm.

关键词： Unsupervised Learning CETCRank Algorithm Important Nodes Large-scale Internet

On Performance Loss of DOA Measurement Using Massive MIMO Receiver with Mixed-ADCs

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arXiv 2021年

作者： Shi, Baihua Zhu, Lingling Cai, Wenlong Chen, Nuo Shen, Tong Zhu, Pengcheng Shu, Feng Wang, Jiangzhou School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing100854 China National Mobile Communications Research Laboratory Southeast University Nanjing China School of Information and Communication Engineering Hainan University Haikou570228 China School of Engineering University of Kent CanterburyCT2 7NT United Kingdom

High hardware cost and high power consumption of massive multiple-input and multiple output (MIMO) are two challenges for the future wireless communications including beyond fifth generation (B5G) and sixth generation (6G). Adopting the low-resolution analog-to-digital converter (ADC) is viewed as a promising solution. Additionally, the direction of arrival (DOA) estimation is an indispensable technology for beam alignment and tracking in massive MIMO systems. Thus, in this paper, the performance of DOA estimation with mixed-ADC structure is firstly investigated. The Cramér-Rao lower bound (CRLB) for this architecture is derived based on the additive quantization noise model. Eventually, a performance loss factor and the associated energy efficiency factor is defined for analysis in detail. Simulation results show that the mixed-ADC architecture can strike a good balance among performance loss, circuit cost and energy efficiency. More importantly, just a few bits (up to 4 bits) of low-resolution ADCs can achieve a satisfactory performance for DOA measurement. Copyright © 2021, The Authors. All rights reserved.

关键词： Direction of arrival

SEGIVE: A Practical Framework of Secure GPU Execution in Virtualization Environment

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SEGIVE: A Practical Framework of Secure GPU Execution in Vir...

IEEE International Conference on Performance, Computing and communications (IPCCC)

作者： Ziyang Wang Fangyu Zheng Jingqiang Lin Guang Fan Jiankuo Dong State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Data Assurance and Communication Security Research Center CAS Beijing China School of Cyber Security University of Science and Technology of China Hefei School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728198293

ISBN: (纸本)9781728198309

With the advancement of processor technology, general-purpose GPUs have become popular parallel computing accelerators in the cloud. However, designed for graphics rendering and high-performance computing, GPUs are born without sound security mechanisms. Consequently, the GPU-based service in the cloud is vulnerable to attacks from the potentially compromised guest OS as large amounts of sensitive code and data are offloaded directly to the unprotected *** this paper, we propose SEGIVE, a practical framework of secure GPU execution in the virtualization environment, which protects offloaded device code and data from disclosure or tampering by malicious guest OSes through the full life cycle of security-critical GPU applications. First, SEGIVE secures all the traffic transferred to GPUs with Intel SGX technology, including the users' sensitive data and GPU binaries. Second, with various memory isolation mechanisms, SEGIVE enhances security in multi-user execution scenarios by sharing a GPU among multiple workloads, which avoids underutilization of device resources. Besides, SEGIVE requires no modifications to application source codes, the GPU architecture, or I/O interconnection to fulfill security principles, and thus almost all prevailing GPU-based applications can easily benefit from SEGIVE with little porting effort. We have implemented SEGIVE with KVM-QEMU on off-the-shelf NVIDIA GPUs and CPUs. Evaluation results show that with security-enhances, the performance of SEGIVE prototype is still competitive to the native execution on compute-intensive applications, especially for the public-key cryptography algorithm.

关键词： Cloud computing Graphics processing units Computer architecture Public key cryptography Software security Virtualization

Variational quantum process tomography

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arXiv 2021年

作者： Xue, Shichuan Liu, Yong Wang, Yang Zhu, Pingyu Guo, Chu Wu, Junjie Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China College of Information and Communication National University of Defense Technology Xi'an710006 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China

Quantum process tomography is an experimental technique to fully characterize an unknown quantum process. Standard quantum process tomography suffers from exponentially scaling of the number of measurements with the increasing system size. In this work, we put forward a quantum machine learning algorithm which approximately encodes the unknown unitary quantum process into a relatively shallow depth parametric quantum circuit. We demonstrate our method by reconstructing the unitary quantum processes resulting from the quantum Hamiltonian evolution and random quantum circuits up to 8 qubits. Results show that those quantum processes could be reconstructed with high fidelity, while the number of input states required are at least 2 orders of magnitude less than required by the standard quantum process tomography. Copyright © 2021, The Authors. All rights reserved.

关键词： Tomography

Outage Performance of Uplink Rate Splitting Multiple Access with Randomly Deployed Users

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arXiv 2022年

作者： Lu, Huabing Xie, Xianzhong Shi, Zhaoyuan Lei, Hongjiang Zhao, Nan Cai, Jun The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing246011 China The School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada

With the rapid proliferation of smart devices in wireless networks, more powerful technologies are expected to fulfill the network requirements of high throughput, massive connectivity, and diversify quality of service. To this end, rate splitting multiple access (RSMA) is proposed as a promising solution to improve spectral efficiency and provide better fairness for the next-generation mobile networks. In this paper, the outage performance of uplink RSMA transmission with randomly deployed users is investigated, taking both user scheduling schemes and power allocation strategies into consideration. Specifically, the greedy user scheduling (GUS) and cumulative distribution function (CDF) based user scheduling (CUS) schemes are considered, which could maximize the rate performance and guarantee scheduling fairness, respectively. Meanwhile, we re-investigate cognitive power allocation (CPA) strategy, and propose a new rate fairness-oriented power allocation (FPA) strategy to enhance the scheduled users' rate fairness. By employing order statistics and stochastic geometry, an analytical expression of the outage probability for each scheduling scheme combining power allocation is derived to characterize the performance. To get more insights, the achieved diversity order of each scheme is also derived. Theoretical results demonstrate that both GUS and CUS schemes applying CPA or FPA strategy can achieve full diversity orders, and the application of CPA strategy in RSMA can effectively eliminate the secondary user's diversity order constraint from the primary user. Simulation results corroborate the accuracy of the analytical expressions, and show that the proposed FPA strategy can achieve excellent rate fairness performance in high signal-to-noise ratio region. Copyright © 2022, The Authors. All rights reserved.

关键词： Wireless networks

Dynamics and Resonance Fluorescence from a Superconducting Artificial Atom Doubly Driven by Quantized and Classical Fields

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arXiv 2024年

作者： Ruan, Xinhui Wang, Jia-Heng He, Dong Song, Pengtao Li, Shengyong Zhao, Qianchuan Kuang, L.M. Tsai, Jaw-Shen Zou, Chang-Ling Zhang, Jing Zheng, Dongning Astafiev, O.V. Liu, Yu-Xi Peng, Zhihui Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center of Quantum Effects and Applications Hunan Normal University Changsha410081 China School of Integrated Circuits Tsinghua University Beijing100084 China Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100190 China Center for Quantum Computing RIKEN Saitama351-0198 Japan Graduate School of Science Tokyo University of Science 1-3 Kagurazaka Shinjuku Tokyo162-0825 Japan CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei Anhui230026 China Hefei National Laboratory Hefei230088 China School of Automation Science and Engineering Xi'an Jiaotong University Xi’an710049 China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China Skolkovo Institute of Science and Technology Nobel str. 3 Moscow143026 Russia Moscow Institute of Physics and Technology Institutskiy Pereulok 9 Dolgoprudny141701 Russia Royal Holloway University of London Surrey EghamTW20 0EX United Kingdom

We report an experimental demonstration of resonance fluorescence in a two-level superconducting artificial atom under two driving fields coupled to a detuned cavity. One of the fields is classical and the other is varied from quantum (vacuum fluctuations) to classical one by controlling the photon number inside the cavity. The device consists of a transmon qubit strongly coupled to a one-dimensional transmission line and a coplanar waveguide resonator. We observe a sideband anti-crossing and asymmetry in the emission spectra of the system through a one-dimensional transmission line, which is fundamentally different from the weak coupling case. By changing the photon number inside the cavity, the emission spectrum of our doubly driven system approaches to the case when the atom is driven by two classical bichromatic fields. We also measure the dynamical evolution of the system through the transmission line and study the properties of the first-order correlation function, Rabi oscillations and energy relaxation in the system. The study of resonance fluorescence from an atom driven by two fields promotes understanding decoherence in superconducting quantum circuits and may find applications in superconducting quantum computing and quantum networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Electric lines

Lightweight Distributed Attribute Based Keyword Search System for Internet of Things 12th

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Lightweight Distributed Attribute Based Keyword Search Syste...

12th International Conference on security, Privacy, and Anonymity in Computation, communication, and Storage, SpaCCS 2019

作者： Long, Jiahuan Zhang, Ke Wang, Xiaofen Dai, Hong-Ning School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Science and Technology on Electronic Information Control Laboratory Chengdu610000 China Department of Information Technology Macau University of Science and Technology Macau China

ISBN: (纸本)9783030248994

Internet of Things (IoT) is a promising networking paradigm that connects various kinds of sensors and exchanges data from smart devices. Since IoT always related to user’s daily life, the problems of security and privacy are of great importance. Presently, the attribute based encryption (ABE) is a popular solution to guarantee the fine-grained sharing of encrypted data in IoT. In this paper, an attribute based keyword search with lightweight decryption in multi-authority (ABKS-LD-MA) is proposed. Our system supports multi-keyword search in cloud by using searchable attribute based encryption. We also integrate the lightweight decryption to searchable ABE scheme that largely reduces the computing overhead for users. Furthermore, our ABKS-LD-MA scheme supports multi-authority scenario, which is more adaptive to the real IoT environment. The experiment analysis shows that our scheme has relatively lowered the communication cost on IoT devices. © 2019, Springer Nature Switzerland AG.

关键词： Internet of things

SigNet: A novel deep learning framework for radio signal classification

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arXiv 2020年

作者： Chen, Zhuangzhi Cui, Hui Xiang, Jingyang Qiu, Kunfeng Huang, Liang Zheng, Shilian Chen, Shichuan Xuan, Qi Yang, Xiaoniu The Institute of Cyberspace Security The College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The College of Computer Science and Technology Zhejiang University of Technology Hangzhou China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China The Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518000 China The Utron Technology Co. Ltd. [as Hangzhou Qianjiang Distinguished Expert Hangzhou310056 China The Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China

Deep learning methods achieve great success in many areas due to their powerful feature extraction capabilities and end-to-end training mechanism, and recently they are also introduced for radio signal modulation classification. In this paper, we propose a novel deep learning framework called SigNet, where a signal-to-matrix (S2M) operator is adopted to convert the original signal into a square matrix first and is co-trained with a follow-up CNN architecture for classification. This model is further accelerated by integrating 1D convolution operators, leading to the upgraded model SigNet2.0. The simulations on two signal datasets show that both SigNet and SigNet2.0 outperform a number of well-known baselines. More interestingly, our proposed models behave extremely well in small-sample learning when only a small training dataset is provided. They can achieve a relatively high accuracy even when 1% training data are kept, while other baseline models may lose their effectiveness much more quickly as the datasets get smaller. Such result suggests that SigNet/SigNet2.0 could be extremely useful in the situations where labeled signal data are difficult to obtain. The visualization of the output features of our models demonstrates that our model can well divide different modulation types of signals in the feature hyper-space. Copyright © 2020, The Authors. All rights reserved.

关键词： Matrix algebra