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A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models

IEEE Transactions on Cognitive communications and Networking 2024年

作者： Lu, Yao Zhu, Yutao Li, Yuqi Xu, Dongwei Lin, Yun Xuan, Qi Yang, Xiaoniu Zhejiang University of Technology Binjiang Institute of Artificial Intelligence Hangzhou310056 China Chinese Academy of Sciences Institute of Computing Technology Beijing China Harbin Engineering University College of Information and Communication Engineering Harbin China Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou China Peng Cheng Laboratory PCL Research Center of Networks and Communications Shenzhen518000 China Hangzhou310056 China Science and Technology on Communication Information Security Control Laboratory Jiaxing China

With the successful application of deep learning in communications systems, deep neural networks are becoming the preferred method for Automatic Modulation Recognition (AMR). Although these AMR models yield impressive results, they often come with high computational complexity and large model sizes, which hinders their practical deployment in communication systems. To address this challenge, we propose a novel layer pruning method, PSR. Specifically, we decompose the AMR model into several consecutive blocks, each containing consecutive layers with similar semantics. Then, we identify layers that need to be preserved within each block based on their contribution. Finally, we reassemble the pruned blocks and fine-tune the compact model. Extensive experiments on five datasets demonstrate the efficiency and effectiveness of PSR over a variety of state-of-the-art baselines, including layer pruning and channel pruning methods. © 2015 IEEE.

关键词： Signal modulation

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P2P transaction method for distributed energy prosumers based on reputation value

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Global Energy Interconnection 2023年第3期6卷 308-323页

作者： Tao Jiang Ting Hua Hao Xiao Linbo Fu Wei Pei Tengfei Ma Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012P.R.China Institute of Electrical Engineering Chinese Academy of Sciences Beijing 100190P.R.China School of Electronic Electrical and Communication Engineering(EECE)University of Chinese Academy of SciencesBeijing 100049P.R.China

Adding a reputation incentive system to peer-to-peer(P2P)energy transactions can encourage prosumers to regulate their trading behavior,which is important for ensuring the efficiency and reliability of P2P *** study proposed a P2P transaction mechanism and game optimization model for prosumers involved in distributed energy sources considering reputation-value ***,the deviation of P2P transactions and the non-consumption rate of distributed renewable energy in P2P transactions were established as indicators to quantify the influencing factors of the reputation value,and a reputation incentive model of P2P transactions for prosumers was ***,the penalty coefficient was applied to the cost function of the prosumers,and a non-cooperative game model of P2P transactions based on the complete information of multi-prosumers was ***,the Nash equilibrium problem was transformed into a nonlinear optimization problem by constructing the modified optimal reaction function,and the Nash equilibrium solution of the game was obtained via a relaxation ***,the modified IEEE 33-node test system based on electricity market P2P and an IEEE 123-node test system were used to analyze and verify the cost and P2P participation of prosumers considering the reputation *** results show that the addition of the reputation incentive system can encourage prosumers to standardize their interactive transaction behavior and actively participate in P2P *** can also improve the operation efficiency of the power grid and promote the perfection of the P2P transaction mechanism.

关键词： P2P Prosumer Non-cooperative game Reputation value Multi-agent of interest Operation optimization

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Pilot decontamination in massive MIMO systems based on pilot design and allocation

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International Journal of Performability engineering 2019年第10期15卷 2753-2761页

作者： Dong, Jingwei Han, Zhiyu Han, Chuang School of Measure-Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China

Pilot contamination has become an important factor affecting the development of massive MIMO. To mitigate pilot contamination, a new scheme of joint polar coordinate pilot assignment and pseudo random code pilot design is proposed in this paper. The scheme allocates the pilot according to the size of the user's polar angle, which is marked with polar coordinates, and uses pseudo random codes with different time delays to add disturbance to users that share the same pilot sequence and users of neighboring cells so as to improve the accuracy of channel estimation. Through theoretical derivation and data simulation analysis of minimum mean square error (MMSE) channel estimation of the system, the scheme can significantly reduce the mean square error (MSE) of channel estimation and improve the signal-to-noise ratio and average transmission rate of uplink transmission. Thus, the adverse effect of pilot contamination on channel estimation is reduced, and the channel throughput is improved. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： MIMO systems

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S$^{\text{4}}$TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第2期9卷 3220-3231页

作者： Xiao Wang Ke Tang Xingyuan Dai Jintao Xu Quancheng Du Rui Ai Yuxiao Wang Weihao Gu Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei China Haomo Technology Company Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S $^{\text{4}}$ TP) framework. Specifically, S $^{\text{4}}$ TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV's planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S $^{\text{4}}$ TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs' trajectories as input. With the integration of SADRF, S $^{\text{4}}$ TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S $^{\text{4}}$ TP in terms of safety and rationality. S $^{\text{4}}$ TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%.

关键词： Trajectory Safety Vehicles Trajectory planning Autonomous vehicles Risk management Behavioral sciences

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Multimodal MR Brain Image Segmentation using Hybrid Optimization Techniques for Earlier Prognosis of Tumor

Multimodal MR Brain Image Segmentation using Hybrid Optimiza...

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Advanced Computing and communication Technologies for High Performance Applications

作者： Baiju Karun T. Arun Prasath M. Pallikonda Rajasekaran M Rakhee Department of Instrumentation and Control Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Biomedical Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Computer Science & Engineering School of Engineering Cochin University of Science and Technology Kerala India

Segmentation of medical images is the process of dividing an image into regions that have some characteristics that are homogeneous and consistent. Medical image segmentation for brain images is an important process in the early diagnosis of brain tumors, brain abnormalities, and their treatment planning. When performed by medical specialists, segmentation of Magnetic Resonance Imaging (MRI) takes a long time. This paper needs to propose some new hybrid approaches for image segmentation and classification for early detection and treatment of brain tumors. The conventional fuzzy c-means clustering (FCM) method is susceptible to artifacts; the local spatial information is frequently presented to an objective function to enhance the FCM method's reliability for segmenting the images. To overcome this limitation, the authors proposed the Fast and Robust FCM (FRFCM), an optimized FCM technique that utilizes morphological restoration (MR) and membership filtration which is considerably quicker and much more reliable than classical FCM. Following the clustering procedure, the new Social Spider Optimization (SSO) swarm algorithm relies on the co - operative features of the social spider is employed to obtain the best outcome. The novel integration of FRFCM and SSO has been affirmed through evaluating on numerous different BraTS datasets, yielding improved performance metric values.

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Radio communication Scenarios in 5G-Railways

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China communications 2023年第9期20卷 235-246页

作者： Ruisi He Bo Ai Zhangdui Zhong Mi Yang Chen Huang Ruifeng Chen Jianwen Ding Hang Mi Zhangfeng Ma Guiqi Sun Changzhu Liu State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China Pervasive Communication Research Center Purple Mountain LaboratoriesNanjing 211111China Institute of Computing Technology China Academy of Railway SciencesBeijing 100081China College of Information Engineering Shaoyang UniversityShaoyang 422000China National Research Center of Railway Safety Assessment Beijing Jiaotong UniversityBeijing 100044China Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Jiaotong UniversityBeijing 100044China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong UniversityBeijing 100044China

With the rapid development of railways,especially high-speed railways,there is an increasingly urgent demand for new wireless communication system for *** the mature 5G technology as an opportunity,5G-railways(5G-R)have been widely regarded as a solution to meet the diversified demands of railway wireless *** the design,deployment and improvement of 5GR networks,radio communication scenario classification plays an important role,affecting channel modeling and system performance *** this paper,a standardized radio communication scenario classification,including 18 scenarios,is proposed for *** paper analyzes the differences of 5G-R scenarios compared with the traditional cellular networks and GSM-railways,according to 5G-R requirements and the unique physical environment and propagation *** proposed standardized scenario classification helps deepen the research of 5G-R and promote the development and application of the existing advanced technologies in railways.

关键词： 5G-R scenario classification smart rail-ways

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A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models

arXiv

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arXiv 2024年

作者： Lu, Yao Zhu, Yutao Li, Yuqi Xu, Dongwei Lin, Yun Xuan, Qi Yang, Xiaoniu The Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou China The Binjiang Institute of Artificial Intelligence Zhejiang University of Technology Hangzhou310056 China The Institute of Computing Technology Chinese Academy of Sciences Beijing China The College of Information and Communication Engineering Harbin Engineering University Harbin China The PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518000 China Utron Technology Company Ltd. Hangzhou310056 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing China

—With the successful application of deep learning in communications systems, deep neural networks are becoming the preferred method for signal classification. Although these models yield impressive results, they often come with high computational complexity and large model sizes, which hinders their practical deployment in communication systems. To address this challenge, we propose a novel layer pruning method. Specifically, we decompose the model into several consecutive blocks, each containing consecutive layers with similar semantics. Then, we identify layers that need to be preserved within each block based on their contribution. Finally, we reassemble the pruned blocks and fine-tune the compact model. Extensive experiments on five datasets demonstrate the efficiency and effectiveness of our method over a variety of state-of-the-art baselines, including layer pruning and channel pruning methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Unsupervised Feature Enrichment and Fidelity Preservation Learning Framework for Skeleton-based Action Recognition

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Li, Chuankun Li, Shuai Gao, Yanbo Gao, Xingyu Chen, Ping Li, Jian Li, Wanqing North University of China State Key Laboratory of Dynamic Testing Technology School of Information and Communication Engineering Taiyuan030051 China Shandong University School of Control Science and Engineering School of Software Jinan250100 China Chinese Academy of Sciences Institute of Microelectronics Beijing100029 China University of Wollongong Advanced Multimedia Research Lab WollongongNSW2522 Australia

Unsupervised skeleton-based action recognition has achieved remarkable progress recently. Existing unsupervised learning methods suffer from severe overfitting problem, and thus small networks are used, significantly reducing the representation capability. To address this problem, the overfitting mechanism behind the unsupervised learning for skeleton-based action recognition is first investigated. It is observed that skeleton is already a relatively high-level and low-dimension feature, but not in the same manifold as the features for action recognition. Simply applying the existing unsupervised learning method tends to produce features that discriminate the different samples rather than action classes, resulting in the overfitting problem. To address this problem, this paper proposes an Unsupervised spatial-temporal Feature Enrichment and Fidelity Preservation (U-FEFP) learning framework to generate rich distributed features that contain all the information of a skeleton sample. A spatial-temporal feature transformation subnetwork is developed using channel-wise topology refinement graph convolutional block and graph convolutional gated recurrent unit block as the basic feature extraction network. The unsupervised Bootstrap Your Own Latent-based learning is utilized to generate rich distributed features, and the unsupervised pretext task-based learning is employed to preserve the information contained in the skeleton. The two unsupervised learning ways are collaborated as U-FEFP to produce robust and discriminative representations. Experimental results on four widely used benchmarks, namely NTU-RGB+D-60, PKU-MMD, NTU-RGB+D-120 and UAV-Human dataset, demonstrate that the proposed U-FEFP obtains the best result compared with the state-of-the-art unsupervised learning methods. © 1991-2012 IEEE.

关键词： Convolution

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Research on Autonomous Maneuvering Strategies of UAV Swarm for Saturation Attack

Research on Autonomous Maneuvering Strategies of UAV Swarm f...

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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Zhang, Tingting Zuo, Yi Zhang, Dongning Ding, Fei Huang, Jun Lin, Yun College of Command and Control Engineering Army Engineering University of the Pla Nanjing China Key Laboratory of Information System Requirements Nanjing China Fifty-fourth Research Institute of China Electronics Technology Group Corporation Shijiazhuang China Internet of Things Academy Nanjing University of Posts and Telecommunications Nanjing China Jsti Group Nanjing China Harbin Engineering University Department of Information and Communication Engineering Harbin China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

In order to solve the problem of unmanned aerial Vehicle (UAV) cluster saturation attack, the autonomous maneuver strategy of UAV is studied. Based on the distributed partial observable Markov decision process, the autonomous maneuver decision model of UAV is constructed, and the Recurrent Multi-Agent Deep Deterministic Policy Gradient (RMADDPG) is used to learn the maneuver strategy of UAV cluster saturation attack. Combined with the typical characteristics of saturation attack scenario, two reward functions of global reward and local reward for UAV maneuver are designed. The trained UAVs can effectively perform suicide saturation attack task. According to the mission target design evaluation index of saturation attack, the Monte Carlo analysis method is used to compare the proposed method with particle swarm optimization (PSO) algorithm in three aspects: inter aircraft collision avoidance, NFZ avoidance and simultaneous attack. The experimental results show that the trained UAVs can effectively perform the suicide saturation attack task under the condition of a small amount of communication, which provides a method for the application of multi-agent deep reinforcement learning to UAV cluster operations. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Framework and Key Technologies for High-speed Railway Comprehensive Transport Hub

Framework and Key Technologies for High-speed Railway Compre...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Gang Xiong Wei Zhang Xiaoming Liu Zhiming Yuan Fenghua Zhu Zundong Zhang State Key Laboratory for Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Beijing China School of Electrical and Control Engineering North China University of Technology Beijing China Communication Signal Research Institute China Academy of Railway Science Beijing China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

The surge in urban population density in China has underscored the vital role of high-speed rail and subway systems in urban transit. Comprehensive transportation hubs, notably high-speed rail passenger terminals, have emerged as linchpins for seamless multimodal transit both within and beyond urban centers. China's railway sector vigorously champions the innovative concept of "station-city integration" and has articulated a forward-looking "16-word policy" for modern railway stations: "Seamless integration, environmental friendliness, economic viability, and intelligent accessibility". The judicious allocation of resources and the augmentation of coordination and control capabilities within comprehensive transportation hubs are imperative for the harmonious evolution of urban transit. The implementation of intelligent control systems for transportation hubs has emerged as a pivotal area of research. This study delves into the framework and critical technologies for high-speed railway comprehensive transport hubs, encompassing various components and their intricate interplay, including transport modalities, infrastructure, travelers, environmental factors, and information systems, thus forming a prototypical cyber-physical-social system. Addressing the prevalent and pressing imperative for the advancement of "station-city integration" high-speed railway comprehensive transport hubs, this research endeavor aims to surmount bottleneck challenges and refine the oversight of passenger flows at high-speed rail terminals. The findings hold noteworthy theoretical import and portend promising practical applications.

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