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7th International Conference on Computer and communications, ICCC 2021

作者： Ji, Heng Xu, Wangyang Gan, Lu Xu, Zhengwu Science and Technology On Communication Information Security Control Laboratory Jiaxing314000 China University of Electronic Science and Technology of China Chengdu611731 China Yibin Institute of UESTC Yibin643000 China

ISBN: (纸本)9781665409506

Signal modulation recognition is an important and complicated problem in spectrum monitoring. In recent years, many advanced deep learning methods have been proposed for this problem. However, the high computational cost of deep neural networks largely hinders practical deployment. In this paper, we consider the signal modulation recognition problem via the deep residual network and propose a binary quantization scheme to reduce the model size and memory cost. This is achieved by simultaneously increasing shortcuts and maintaining full-precision weights and outputs of key layers in binary residual network in a simple way. The results show that with competitive accuracy, 95% of multiply-accumulate operations of model are converted into binary operations under 0 dB snr. In summary, the proposed method can effectively reduce the parameters and complexity of recognition model. © 2021 IEEE.

关键词： Deep neural networks

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Modulation Recognition Based on Lightweight Residual Network via Hybrid Pruning 7

Modulation Recognition Based on Lightweight Residual Network...

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7th International Conference on Computer and communications, ICCC 2021

ISBN: (纸本)9781665409506

Signal modulation recognition is an important and complicated problem in spectrum monitoring. In recent years, many advanced deep learning methods have been proposed for this problem. However, the high computational cost of deep neural networks largely hinders practical deployment. In this paper, we consider the signal modulation recognition problem via the deep residual network and propose a hybrid Pruning scheme to reduce the model size and memory cost. This is achieved by simultaneously enforcing channel-level and layer-level sparsity in residual network in a simple way. We take wide and large networks as input models, but during training, insignificant channels and layers are automatically identified and pruned afterwards, yielding compact and shallower networks with comparable accuracy. The results show that with 0 dB snr, the trainable parameters and multiply-accumulate operations are both reduced by 2/3. In summary, the proposed method can effectively reduce the parameters and complexity of recognition model. © 2021 IEEE.

关键词： Deep neural networks

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Adversarial Examples Detection of Electromagnetic Signal Based on GAN

Adversarial Examples Detection of Electromagnetic Signal Bas...

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International Conference on Big Data Computing and communications (BIGCOM)

作者： Jiawei Zhu Jiangpeng Li Dongwei Xu Chuntao Gu Qi Xuan Shunling Wang Security Control Laboratory Science and Technology on Communication Information Jiaxing China Institute of Cyberspace Security Zhejiang University of Technology College of Information Engineering Zhejiang University of Technology Hangzhou China

In the field of signal modulation classification, deep neural networks (DNNs) perform very well and have good generalization ability. However, the DNN model is extremely vulnerable. The model gives a false output with high confidence from an input example, which formed only by deliberately adding tiny disturbances. Such elaborate examples that make the model misclassify are called adversarial examples. In this paper, for the detection of adversarial examples in electromagnetic signals, we propose a method based on generative adversarial networks (GANs), a novel strategy to defend deep neural networks against such attacks. First of all, by experiment we came to the conclusion that the confidence of the DNN model for normal examples is stable, and the confidence for adversarial examples is unstable. We trained a classifier that performs well in extracting electromagnetic signal features. Then, We feed the normal examples to the generative adversarial network, the generator generates examples with similar data distribution to the clean examples. We send such pairs of examples to the classifier to calculate a threshold T, and the same operations will be performed on the test examples. Finally, the absolute error E of each pair of test examples will be calculated and compared with the threshold T to achieve electromagnetic signal adversarial examples detection. We define the method in the paper as Detect-GAN. The experimental results show that Detect-GAN can effectively defend against common and powerful attack algorithm. Comparing with other detection methods, Detect-GAN achieves more competitive performance.

关键词： Deep learning Neural networks Modulation Big Data Generative adversarial networks Feature extraction Generators

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Research on Space-Time Block Code technology in MIMO System

Research on Space-Time Block Code Technology in MIMO System

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International Conference on Computer and communications (ICCC)

作者： Conglin Pan Si Chen Wei Wu Jiachuan Qian Lijun Wang Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of CETC Jiaxing China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665409513

MIMO technology has been widely used in the telecommunication systems nowadays, and the space-time coding is a key part of MIMO technology. A good coding scheme can exploit the spatial diversity to correct the error which is generated in transmission, and increase the normalized transfer rate with low decoding complexity. On the Basis of the research on different Space-Time Block Codes, this essay proposes a new STBC, Diagonal Block Orthogonal Space-Time Block Code. Then we will compare it with other STBCs in the performance of bit error rate, transfer rate, decoding complexity and peek-to-average power ratio, the final result will prove the superiority of DBOAST.

关键词： Spatial diversity Bit error rate Encoding Decoding Complexity theory Telecommunications Block codes

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Bayesian Framework for Multi-User Cooperative Radio SLAM 35

Bayesian Framework for Multi-User Cooperative Radio SLAM

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio communications, PIMRC 2024

作者： Que, Hang Yang, Jie Du, Tao Xia, Shuqiang Wen, Chao-Kai Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing211189 China Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Zte Corporation Shenzhen518001 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518001 China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan

ISBN: (纸本)9798350362244

The advancement of millimeter-wave communication technology heralds new sensing capabilities. By leveraging channel multipath parameter estimates, we can harness simultaneous localization and mapping (SLAM) for precise user equipment (UE) localization and radio map construction in 6 G communication systems. Particularly in multi-UE scenarios, SLAM empowers base stations to amalgamate the local radio maps of various UEs efficiently. This study introduces a novel Bayesian framework specifically designed for multi-UE SLAM, complemented by a tailored factor graph. We also unveil a two-stage multi-UE SLAM algorithm. Our simulation results reveal that this algorithm substantially enhances radio map construction accuracy by 4 8. 5 % and UE localization accuracy by 13.5 %, outperforming single-UE cases. Moreover, the algorithm demonstrates remarkable adaptability to environmental changes, showcasing its potential for long-term evolution in dynamic settings. © 2024 IEEE.

关键词： Long Term Evolution (LTE)

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Self-Interference Cancellation Circuit With Improved Transmission Power Efficiency Based On Unequal Power Divider 13

Self-Interference Cancellation Circuit With Improved Transmi...

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13th International Conference on Microwave and Millimeter Wave technology, ICMMT 2021

作者： Li, Huimin Xiang, Qianyin Huang, Xiaoguo Jia, Dinghong Feng, Quanyuan Southwest Jiaotong University School Of Information Science And Technology Chengdu611756 China Science And Technology On Communication Information Security Control Laboratory Jiaxing314033 China 41st Institute Of CETC Qingdao266555 China

ISBN: (纸本)9781665434379

The paper proposes a self-interference cancellation (SIC) architecture based on unequal power divider. The transmission efficiency of SIC is investigated. A power divider with unequal power dividing ratio is used in the SIC for improving transmission efficiency of circulator isolated transceiver. A four-tap SIC circuit was designed and fabricated. Measurement results show that 60dB isolation is achieved in the frequency band of 915-926MHz, which is an increase of 37dB compared to that of the original circulator. The transmission efficiency (TE) increases from 42.5% (power-dividing ratio is 1:1) to 69.1% (power-dividing ratio is 1:4). © 2021 IEEE.

关键词： Timing circuits

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Breather dynamics in an optomechanical array with staggered coupling

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Physical Review A 2025年第3期111卷 033502-033502页

作者： Yuan Zhao Yiheng Hou Yu Wang Zhuo Fan Wei Peng Qin Zhou Siliu Xu Shihua Chen Key Laboratory of Optoelectronic Sensing and Intelligent Control Hubei University of Science and Technology Xianning 437100 China School of Biomedical Engineering and Imaging Xianning Medical College Hubei University of Science and Technology Xianning 437100 China School of Electronic and Information Engineering Hubei University of Science and Technology Xianning 437100 China Research Center of Nonlinear Science School of Mathematics and Physical Sciences Wuhan Textile University Wuhan 430200 China School of Physics and Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing 211189 China

We present an approach toward generating highly controllable breathers, using an array of staggered optomechanical cavities that feature alternating coupling strengths. Four different types of breather states as well as stable two-soliton states are demonstrated to form in such an optomechanical array, thanks to the interplay between the photon-hopping induced tunneling effect and the photon-phonon coupling in the optomechanical cell. It is seen that both their photonic and phononic wave components manifest quasi-periodic oscillatory patterns and coherent localization. We unveil that such breather dynamics can be well controlled by adjusting the coupling strength, the cavity field decay, and the number of array cells. As a result, we observe an unusual odd-even lattice effect in that stable breathers are only available when the cell number is even. We anticipate that these findings from the staggered optomechanical configuration may expand the realm of cavity optomechanics, yielding promising applications such as on-chip manipulation of light propagation.

关键词： Solitons

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Defend Against Jamming Attacks Using Deep Reinforcement Learning 13

Defend Against Jamming Attacks Using Deep Reinforcement Lear...

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13th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2021

作者： Shen, Weiguo Wang, Wei Jin, Hu Zhang, Weifeng Science and Technology on Communication Information Security Control Laboratory China National University of Defense Technology China Jiaxing University China Jiaxing Deep Intelligence Technology Co. Ltd China

ISBN: (纸本)9781665418126

Cognitive radio network has the learning ability to adjust transmission behavior to adapt to dynamic electromagnetic environment, and it has become a research hotspot in the field of the new generation of intelligent wireless communication systems. However, recent research results show that cognitive radio networks are vulnerable to interference attacks, resulting in a serious decline in spectrum utilization. In this paper, an intelligent anti-jamming scheme based on deep reinforcement learning is proposed, and the anti-jamming decision of the communication party is modeled as a Markov decision chain. This method can help the communication transmitter to learn a combination of communication parameters that can maximize the probability of communication success. In the specific implementation, we use Double DQN as the communication decision-making brain, and specially designed a convolutional neural network suitable for the communication spectrum as input to accurately estimate the action value function. The simulation results show that our method has better performance than the traditional anti-jamming method based on reinforcement learning. © 2021 IEEE.

关键词： Reinforcement learning

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Learning-Driven Transmission Latency Minimization in EH-Relay Assisted IoT Networks

Learning-Driven Transmission Latency Minimization in EH-Rela...

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IEEE Conference on Global communications (GLOBECOM)

作者： Qianru Wang Li Ping Qian Mingqing Li Wei Jiang Yuan Wu Xiaoniu Yang College of Information Engineering Zhejiang University of Technology Hangzhou Zhejiang China State Key Laboratory of Internet of Things for Smart City University of Macau Macao SAR China Science and Technology on Communication Information Security Control Laboratory Jiaxing China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China

Internet of Things (IoT) is one of the key applications of 5G, and the data transmission is the basis of IoT networks. In this paper, we investigate the data transmission scheme in non-orthogonal multiple access (NOMA) for IoT networks to minimize the transmission latency. In order to improve the communication efficiency between devices and the base station (BS) without more energy consumption, we deploy an energy harvesting (EH) relay node between devices and the BS for data transmitting and forwarding. Based on this networking model, we first aim at minimizing the transmission latency by jointly optimizing the transmit power of devices and the relay, forwarding ratios among devices, and forwarding time fraction when transmitting a fixed data bits from devices to the BS via the relay under the constraints of energy buffer and data buffer. Noted that the formulated problem is discrete-continuous mixed and non-convex, we apply the deep deterministic policy gradient (DDPG) algorithm in the framework of bisection searching to obtain the optimal solution. Specifically, the bisection searching is used to seek the possible transmission latency, and the DDPG is to check the feasibility of the chosen transmission latency. Finally, the effectiveness of the proposed model-data-driven algorithm is verified by comparing it with other benchmark algorithms, such as LINGO.

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Energy Minimization with Secrecy Provisioning in Federated Learning-Assisted Marine Digital Twin Networks

Energy Minimization with Secrecy Provisioning in Federated L...

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IEEE International Conference on communications (ICC)

作者： Li Ping Qian Mingqing Li Xinyu Dong Yuan Wu Xiaoniu Yang College of Information Engineering Zhejiang University of Technology Hangzhou Zhejiang China State Key Laboratory of Internet of Things for Smart City University of Macau Macao SAR China Science and Technology on Communication Information Security Control Laboratory Jiaxing China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China

Digital twin has been emerging as a promising paradigm that connects the physical entities and digital space, and continuously evolves and optimizes the physical systems. In this paper, we focus on studying the efficient data communication and computation when constructing the marine digital twin network with secrecy provisioning. Specifically, we leverage the federated learning (FL) to train the digital twin model. In the process of FL, all unmanned surface vehicles (USVs) deliver the trained models with non-orthogonal multiple access (NOMA) to the high altitude platform (HAP) for the global model aggregation. Considering the possible eavesdropping on the HAP, we utilize the chaotic sequences to spread the model information during the global model broadcasting. In this framework, we further want to minimize the total energy consumption of completing the digital twin training by jointly optimizing the global accuracy, local accuracy, HAP's transmission power, and model uploading duration subject to the secrecy provisioning and latency constraint. Despite the non-convexity, we propose a low-complexity search algorithm (LCS-Algorithm) to solve this joint optimization problem. Finally, the numerical results validate the performance of the proposed algorithm in terms of optimality and time efficiency.

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